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Contact

olivier.colliot [at] cnrs [dot] fr

stanley.durrleman [at] inria [dot] fr

Location

Institut du Cerveau
Hôpital Pitié Salpêtrière
47 Boulevard de l\'Hôpital
75013 Paris

Access map

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Making great software, great product that stands the test of time and not just survives but thrives through monumental technological shifts is incredibly hard. That challenge is part of the reason I love doing it. There is never a dull day, and the reward of seeing the code you wrote used by the most amazing creators in the world is an indescribable pleasure. When I see what people create with WordPress, some days I feel like I’m grinding pigment for Leonardo da Vinci or slitting a quill for Beethoven.

In open source, one thing that makes it even harder to ship great software is bringing together disparate groups of contributors who may have entirely different incentives or missions or philosophies about how to make great work. Working together on a team is such a delicate balance, and even one person rowing in the wrong direction can throw everyone else off.

That’s why periodically I think it is very healthy for open source projects to fork, it allows for people to try out and experiment with different forms of governance, leadership, decision-making, and technical approaches. As I’ve said, forking is beautiful, and forks have my full support and we’ll even link and promote them.

Joost is a self-proclaimed leader in the SEO space, an industry known for making the web better. He asked for and I gave him WordPress marketing leadership responsibility in January 2019 and he stepped down in June of 2019, I think we would both agree in those 5 months he was not effective at leading the marketing team or doing the work himself.

Karim leads a small WordPress agency called Crowd Favorite which counts clients such as Lexus and ABC and employs ~50 people.

Both are men I have shared meals with and consider of the highest integrity. I would trust them to watch any of my 15 godchildren for a day. These are good humans. Now go do the work. It probably won’t happen on day one, but Joost and Karim’s fork, which I’ll call JKPress until they come up with a better name, has a number of ideas they want to try out around governance and architecture. While Joost and Karim will be unilaterally in charge in the beginning, it sounds like they want to set up:

  1. A non-profit foundation, with a broad board to control their new project.
  2. A website owned by that foundation which hosts community resources like a plugin directory, forums, etc.
  3. No more centralized and moderated plugin and theme directories with security guidelines or restrictions are what plugins are allowed to do like putting banners in your admin or gathering data, everything done in a federated/distributed manner.
  4. The trademarks for their new project will either be public domain or held by their foundation.
  5. “Modernization” of the technology stack, perhaps going a Laravel-like approach or changing how WordPress’ architecture works.
  6. Teams and committees to make decisions for everything, so no single person has too much power or authority.

Karim has a similar post. Joost says he has the time and energy to lead:

Now, as core committer Jb Audras (not employed by me or Automattic) points out, within WordPress we have a process in which people earn the right to lead a release:

However in Joost and Karim’s new project, they don’t need to follow our process or put in the hours to prove their worth within the WordPress.org ecosystem, they can just lead by example by shipping code and product to people that they can use, evaluate, and test out for themselves. If they need financial or hosting support is sounds like WP Engine wants to support their fork:

Awesome! (Maybe it’s so successful they rebrand as JK Engine in the future.) WP Engine, with its half a billion in revenue and 1,000+ employees, has more than enough resources to support and maintain a legitimate fork of WordPress. And they are welcome to use all the GPL code myself and others have created to do so, including many parts of WordPress.org that are open source released under the GPL, and Gutenberg which is GPL + MPL.

Joost also is a major investor (owner?) in Post Status (which he tried to sell to me a few months ago, and I declined to buy, perhaps kicking off his consternation with me), so they have a news media site and Slack instance already ready to go. He also is an investor in PatchStack and appears to be trying to create a new business around something called Progress Planner, both of which could be incorporated into the new non-profit project to give them some competitive distinctions from WordPress.

To make this easy and hopefully give this project the push it needs to get off the ground, I’m deactivating the .org accounts of Joost, Karim, Se Reed, Heather Burns, and Morten Rand-Hendriksen. I strongly encourage anyone who wants to try different leadership models or align with WP Engine to join up with their new effort.

In the meantime, on top of my day job running a 1,700+ person company with 25+ products, which I typically work 60-80 hours a week on, I’ll find time on nights and weekends to work on WordPress 6.8 and beyond. Myself and other “non-sponsored” contributors have been doing this a long time and while we may need to reduce scope a bit I think we can put out a solid release in March.

Joost and Karim have a number of bold and interesting ideas, and I’m genuinely curious to see how they work out. The beauty of open source is they can take all of the GPL code in WordPress and ship their vision. You don’t need permission, you can just do things. If they create something that’s awesome, we may even merge it back into WordPress, that ability for code and ideas to freely flow between projects is part of what makes open source such an engine for innovation. I propose that in a year we do a WordPress + JKPress summit, look at what we’ve shipped and learned in the process, which I’d be happy to host and sponsor in NYC next January 2026. The broader community will benefit greatly from this effort, as it’s giving us a true chance to try something different and see how it goes.

";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:30:"com-wordpress:feed-additions:1";a:1:{s:7:"post-id";a:1:{i:0;a:5:{s:4:"data";s:5:"18394";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:1;a:6:{s:4:"data";s:57:" ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:67:"WordPress Themes Need More Weird: A Call for Creative Digital Homes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:102:"https://wordpress.org/news/2025/01/wordpress-themes-need-more-weird-a-call-for-creative-digital-homes/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Thu, 02 Jan 2025 18:53:06 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:1:{i:0;a:5:{s:4:"data";s:6:"Design";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:35:"https://wordpress.org/news/?p=18358";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:409:"The modern web has gradually shifted from a vibrant tapestry of personal expression to a landscape of identical designs, where millions of websites share not just similar structures, but identical visual language, spacing, and interaction patterns. As we collectively gravitate toward the same “proven” layouts and “conversion-optimized” designs, we’re not just losing visual diversity – […]";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:10:"Nick Hamze";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:7631:"

The modern web has gradually shifted from a vibrant tapestry of personal expression to a landscape of identical designs, where millions of websites share not just similar structures, but identical visual language, spacing, and interaction patterns. As we collectively gravitate toward the same “proven” layouts and “conversion-optimized” designs, we’re not just losing visual diversity – we’re ceding control over how we present ourselves to the world. This matters because genuine self-expression online isn’t just about aesthetics – it’s about maintaining spaces where authentic voices can flourish. 

When every blog has the same hero section, when every portfolio follows the same grid, when every restaurant site looks interchangeable, we create an echo chamber of sameness. The cost isn’t just visual monotony – it’s the slow erosion of the web’s ability to surprise, delight, and showcase truly individual perspectives. WordPress, with its emphasis on complete ownership and control, offers an opportunity to break free from this convergence of design, allowing creators to build digital spaces that truly reflect their unique voice and vision.

Think of WordPress themes like album covers. They should have personality and create an immediate visual impact. The web has become too sanitized, with everyone chasing the same minimal, “professional” look.

Great themes should:

We need more themes that make people say “Wow!” or “That’s different!” rather than “That’s clean and professional.” The web needs more personality, more risk-taking, more fun.

After spending countless hours digging through the WordPress theme repository, searching for designs that break the mold and spark excitement, I came up nearly empty-handed. Don’t get me wrong – there are plenty of well-built themes out there. But where’s the daring? The personality? The unexpected?

If you’ve got a wild theme idea burning in your mind – that portfolio theme that looks like a vintage trading card collection, that blog theme inspired by zine culture, that restaurant theme that feels like a hand-drawn menu – now’s the time to build it. WordPress desperately needs your creativity, your weird ideas, your willingness to break the visual rules. The future of the web shouldn’t be a monochrome landscape of identical layouts. Let’s make WordPress themes exciting again. Let’s make the web weird again.

";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:30:"com-wordpress:feed-additions:1";a:1:{s:7:"post-id";a:1:{i:0;a:5:{s:4:"data";s:5:"18358";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:2;a:6:{s:4:"data";s:57:" ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:13:"Holiday Break";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:49:"https://wordpress.org/news/2024/12/holiday-break/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Fri, 20 Dec 2024 00:36:59 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:1:{i:0;a:5:{s:4:"data";s:7:"General";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:35:"https://wordpress.org/news/?p=18328";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:362:"In order to give myself and the many tired volunteers around WordPress.org a break for the holidays, we’re going to be pausing a few of the free services currently offered: We’re going to leave things like localization and the forums open because these don’t require much moderation. As you may have heard, I’m legally compelled […]";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:14:"Matt Mullenweg";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:2315:"

In order to give myself and the many tired volunteers around WordPress.org a break for the holidays, we’re going to be pausing a few of the free services currently offered:

We’re going to leave things like localization and the forums open because these don’t require much moderation.

As you may have heard, I’m legally compelled to provide free labor and services to WP Engine thanks to the success of their expensive lawyers, so in order to avoid bothering the court I will say that none of the above applies to WP Engine, so if they need to bypass any of the above please just have your high-priced attorneys talk to my high-priced attorneys and we’ll arrange access, or just reach out directly to me on Slack and I’ll fix things for you.

I hope to find the time, energy, and money to reopen all of this sometime in the new year. Right now much of the time I would spend making WordPress better is being taken up defending against WP Engine’s legal attacks. Their attacks are against Automattic, but also me individually as the owner of WordPress.org, which means if they win I can be personally liable for millions of dollars of damages.

If you would like to fund legal attacks against me, I would encourage you to sign up for WP Engine services, they have great plans and pricing starting at $50/mo and scaling all the way up to $2,000/mo. If not, you can use literally any other web host in the world that isn’t suing me and is offering promotions and discounts for switching away from WP Engine.

";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:30:"com-wordpress:feed-additions:1";a:1:{s:7:"post-id";a:1:{i:0;a:5:{s:4:"data";s:5:"18328";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:3;a:6:{s:4:"data";s:60:" ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:57:"State of the Word 2024: Legacy, Innovation, and Community";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:90:"https://wordpress.org/news/2024/12/state-of-the-word-2024-legacy-innovation-and-community/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Mon, 16 Dec 2024 21:28:22 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:2:{i:0;a:5:{s:4:"data";s:6:"Events";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}i:1;a:5:{s:4:"data";s:17:"state of the word";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:35:"https://wordpress.org/news/?p=18205";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:282:"On a memorable evening in Tokyo, State of the Word 2024 brought together WordPress enthusiasts from around the world—hundreds in person and millions more online. This event marked the first time State of the Word was hosted in Asia, reflecting the platform\'s growing global reach.";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:17:"Nicholas Garofalo";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:88600:"

On a memorable evening in Tokyo, State of the Word 2024 brought together WordPress enthusiasts from around the world—hundreds in person and millions more online. This event marked the first time State of the Word was hosted in Asia, reflecting the platform’s growing global reach. The setting couldn’t have been more fitting: a city where tradition and technology coexist in seamless harmony. Tokyo, much like WordPress itself, reflects a powerful blend of legacy and innovation, craftsmanship and technology, and moments of vast scale balanced by serene stillness.

Tokyo is a city you feel.

Matt Mullenweg, WordPress Cofounder

During the event, the concept of kansei engineering emerged as a central theme. This Japanese design philosophy seeks to create experiences that go beyond function and aesthetics, focusing on how something feels. As highlighted during the keynote, this principle has quietly influenced WordPress’s development, shaping its design and user experience in ways that resonate on an instinctive level.

The evening also celebrated Japan’s deep-rooted connection to WordPress. Nearly 21 years ago, Japan became the first country to localize WordPress, long before a formal translation framework existed. It all started with a single forum post from a user named Otsukare, launching a translation project that helped WordPress become a truly global platform. Seeing how far the Japanese WordPress community has come—both in market share and cultural influence—was a powerful reminder of what shared purpose can achieve.

Wapuu, WordPress’s beloved mascot, was also born in Japan. What began as a simple idea for a fun and friendly representation of WordPress evolved into a global phenomenon. Thanks to Kazuko Kaneuchi’s generous open-source contribution, Wapuu has been reimagined by WordPress communities worldwide, each version infused with local character. This uniquely Japanese creation has helped make WordPress more welcoming, approachable, and fun wherever it appears.

WordPress Growth in 2024

WordPress cofounder Matt Mullenweg highlighted significant achievements that underscored WordPress’s growth, resilience, and expanding global presence in 2024. He shared that WordPress now powers 43.6% of all websites globally. In Japan, WordPress’s influence is even more pronounced, powering 58.5% of all websites. This remarkable statistic reinforces the platform’s enduring role as a cornerstone of the open web and accentuates Japan’s deep-rooted commitment to the WordPress ecosystem and its developers’ significant contributions.

WordPress sites using languages other than English are expected to surpass English-language sites by 2025. German recently overtook Japanese as the third-most-used language, though Japanese remained close behind. Meanwhile, emerging languages like Farsi experienced rapid adoption, reflecting the platform’s expanding multilingual ecosystem. In Southeast Asia, languages such as Indonesian, Vietnamese, and Thai saw substantial year-over-year growth, signaling broader adoption across diverse regions.

Core downloads surged to nearly half a billion annually, with the notable releases of WordPress 6.5, 6.6, and 6.7.

WordPress’s design and development ecosystem flourished as well. Over 1,700 new themes were uploaded in 2024, bringing more than 1,000 block themes to the official repository and reflecting increased interest in modern, flexible site design.

The plugin ecosystem also saw record-breaking activity this year. Plugin downloads surged toward 2.35 billion, representing a 20% year-over-year increase. Plugin updates exceeded 3 billion and are on track to surpass 3.5 billion by year’s end. Notably, the Plugin Review Team made transformative improvements, drastically reducing the average review wait time. Their efficiency gains were complemented by the launch of the Plugin Check tool, which reduced submission issues by 41% while enabling the team to approve 138% more plugins each week.

These accomplishments showcase WordPress’s resilience, adaptability, and ever-expanding influence. As the platform continues to evolve, its global community remains at the heart of its success, driving innovation and ensuring that WordPress thrives as the leading tool for building the open web.

Help shape the future of WordPress: Join a contributor team today!

Advancing the Platform

WordPress lead architect, Matías Ventura, highlighted WordPress’s evolution through the lenses of writing, design, building, and development, demoing various pieces of new and forthcoming enhancements.

Writing

The writing experience in WordPress saw notable advancements this year, with an improved distraction-free mode that helps users to focus on content creation without interface distractions. Now you can directly select the image itself to drag and drop it where you want, even enabling on-the-fly gallery creation when you drop images next to each other.

Additionally, the introduction of block-level comments in the editor, currently an experimental feature, promises to reshape collaborative workflows by enabling teams to leave notes directly on blocks.

These enhancements all work together to make writing, composing, and editing in WordPress feel more fluid, personal, and pleasant than ever.

Design

Along with new default theme Twenty Twenty-Five, more than 1,000 block themes offer tailored starting points for different site types, including portfolios, blogs, and business sites. Designers can also utilize the improved Style Book for a comprehensive view of their site’s appearance, ensuring a smooth design process.

Design work isn’t just about aesthetics—it’s also about creating the right environment and guardrails. It’s important that users can interact with their site, add content, replace media, and choose sections without needing to know the layout details. We’re implementing better default experiences to help you focus exclusively on the content or on the design, depending on your needs at the moment. 

This all works seamlessly with the zoom-out view, where users can compose content using patterns without having to set up every individual block. Having a bird’s-eye view of your site can really help you gain a different perspective.

These design capabilities scale with you as your WordPress projects grow. WordPress’s approach to design is systematic: blocks combine to form patterns, patterns form templates, and templates help separate content from presentation.

Building

WordPress’s content management capabilities allow working at scale and across teams. Central to this is the introduction of Block Bindings, which merge the flexibility of blocks with the structured power of meta fields. This feature allows block attributes to be directly linked to data sources like post meta, reducing the need for custom blocks while creating deeper, more dynamic content relationships. The familiar block interface remains intact, making complex data management feel seamless. This connects naturally with our broader work on Data Views for post types and meta fields. 

These updates reinforce WordPress’s role as a powerful content management system by connecting its core primitives—blocks, post types, taxonomies, and meta fields—more intuitively. 

Development

Lastly, Matías showcased a range of groundbreaking tools that empower WordPress developers and streamline their workflows. One of the highlights was the new Templates API, which has simplified the process of registering and managing custom templates. Future updates to the API will allow users to register and activate templates seamlessly, enabling dynamic site customizations such as scheduling different homepage templates for special events or swapping category archives during campaigns. This flexible approach offers developers greater creative control in a standardized way. 

The session also explored the Interactivity API, designed to deliver fast, seamless website experiences by enabling server-rendered interactivity within WordPress. Unlike JavaScript-heavy frameworks, this technology keeps everything within WordPress’s existing ecosystem, bridging the gap between developers and content creators. Attendees saw live demos showcasing instant search, pagination, and commenting—all without page reloads—while maintaining a perfect performance score of 100 on Lighthouse. In addition, it was announced that responsive controls will receive significant attention, with new features being explored, like block visibility by breakpoint and adding min/max controls to the columns block.

The WordPress Playground also emerged as a game-changer, allowing users to spin up WordPress sites directly in their browsers, experiment with Blueprints, and manage projects offline. With improved GitHub integration and expanded documentation, WordPress developers now have a more accessible and powerful toolkit than ever before.

An AI Future

Returning to the stage, Matt noted that Gutenberg’s evolution is paving the way for AI-powered site building while keeping creative control in users’ hands. A recent speed building challenge on WordPress’s YouTube channel showcased this potential, with Nick Diego using AI-assisted tools and Ryan Welcher building manually. While the AI-assisted approach won, the key takeaway was that AI isn’t here to replace developers but to enhance creativity and efficiency.

Community Impact and Global Reach

When WordPress Executive Director Mary Hubbard took the stage, she emphasized WordPress’s commitment to its open-source mission and the power of its global community. Mary shared her passion for defending WordPress’s principles, reaffirming that when users choose WordPress, they should receive the authentic, community-driven experience that the platform stands for. This commitment to clarity, trust, and open-source integrity is central to ensuring WordPress’s long-term sustainability and success.

In 2024, WordPress’s global influence surged through expanded educational programs, developer contributions, and grassroots initiatives. The platform’s social media following grew to 2.3 million, while major events like WordCamps and live-streamed gatherings attracted millions of attendees and viewers, connecting people worldwide.

Learn WordPress introduced Structured Learning Pathways, offering tailored tracks for beginners and developers, fostering a growing network of creators eager to learn and contribute. Grassroots programs flourished, with WP Campus Connect bringing WordPress education to Indian colleges and innovation competitions in Uganda empowering young creators. In Latin America, the Community Reactivation Project reignited meetups across nine cities, fostering a network of over 150 active members and setting the stage for three new WordCamps in 2025.

WordPress’s efforts also advanced through Openverse, which expanded its free content library to 884 million images and 4.2 million audio files, serving millions of creators worldwide and supporting WordPress’s broader mission of democratizing publishing.

Whether through educational platforms, developer-driven innovation, or community-led projects, WordPress’s ecosystem continues to nurture shared learning, creativity, and collaboration, ensuring its growth and relevance for future generations.

Japanese Community Highlights

Junko Fukui Nukaga—Community Team rep, program manager, and WordCamp organizer—noted that WordPress’s prominence in Japan contributes to an economy now estimated to exceed 100 billion yen.

In October of 2024, the Japanese WordPress community celebrated DigitalCube’s IPO on the Tokyo PRO Market, marking a milestone for the local WordPress ecosystem. Major contributors like Takayuki Miyoshi’s Contact Form 7 plugin surpassed 10 million active users, while companies like Sakura Internet and XServer built specialized WordPress infrastructure.

Community events in Japan have also flourished, with 189 local meetups held throughout the year, fueled by dedicated volunteers and organizers. Translation Night gatherings have ensured WordPress remains accessible to Japanese users, reflecting a thriving collaborative spirit.

Matt gave special recognition to Japan’s standout contributor, Aki Hamano, a Core Committer whose exceptional efforts elevated WordPress development over the past year. Hamano-san made an impressive 774 contributions to WordPress core, earning 162 props for WordPress 6.5, rising to 274 props for 6.6 as the second-highest contributor, and securing the top spot with 338 props for 6.7.Other notable Japanese contributors included Akira Tachibana, an active Docs Team member, and Nukaga, recognized for her exceptional community organizing efforts. Additionally, 13 Japanese contributors supported 5.4% of WordPress 6.6 development, showcasing the country’s growing influence in the WordPress ecosystem.

Data Liberation

Reflecting on the progress since the initiative’s launch last year, the focus remained on ensuring that WordPress not only becomes more powerful but also embodies freedom in its deepest sense—the freedom to move content anywhere, collaborate without limits, and create without constraints. This vision extends beyond individual sites to a broader web where content flows seamlessly across platforms, enabling unrestricted creativity and innovation.

One compelling example demonstrated how easily ePub files could be imported into a WordPress site, integrating seamlessly with existing designs. This represents the initiative’s broader goal: making content migration and integration effortless. WordPress Playground plays a critical role in this vision by enabling easy site migration through a simple browser extension. With Playground as a staging area, migrating and adapting sites becomes intuitive and accessible.

Q&A

The floor was opened to questions in both Japanese and English.

Questions from the audience, including Tokyo Vice author Jake Adelstein, covered the future of blogging, WordPress performance, the impact of AI search, and what democratizing publishing means today. Matt shared his excitement for more open platforms such as Mastodon and Bluesky, as well as his recommendations for optimizing your site for both humans and AI. A common thread throughout was that a personal website is an important part of your digital identity, and WordPress allows you to express yourself in fun and unique ways.

Panels

After attendees enjoyed a special performance by the pianist, Takai-san, industry leaders, creators, and innovators took the stage for panel discussions about the present and future of WordPress, moderated by Mary Hubbard.

Publishing in the Open

Featuring:

This first panel explored the transformative power of open-source publishing. Panelists shared insights into how open publishing has influenced their creative journeys, expanded audience engagement, and shaped storytelling across cultural boundaries.

Publishing in the open has defined what I’ve done. All the best connections I’ve made in live have been the result of publishing in the open. – Craig Mod

Publishing in the open, like WordPress, is about building community, mutual connections, and putting power back into the hands of creators.

The Future of WordPress in Japan and Beyond

Featuring:

The second discussion highlighted WordPress’s remarkable growth in Japan and its broader global impact. The discussion covered the drivers behind Japan’s adoption of WordPress, its thriving ecosystem of WordPress-based businesses, and emerging trends in web development.

Compared to other CMSs the WordPress Japanese is much easier to use. – Hajime Ogushi

The group discussed plugins such as Contact Form 7, the affordability of hosting WordPress, and local meetups and events

Closing

Thank you to all the guests who joined us on stage, those who ventured to Tokyo, and everyone who tuned in from around the world. Today’s event showcased how a free and infinitely flexible platform, an active global community, open innovation, and a commitment to a fully democratized web make us better at being who we are.

From Tokyo, Arigatou Gozaimashita!

For those interested in exploring past State of the Word keynotes, WordPress has curated a comprehensive YouTube playlist featuring keynotes from previous years. Watch them all here: State of the Word YouTube Playlist. Be sure to mark your calendars for major WordPress events in 2025: WordCamp Asia (Manila, Philippines), WordCamp Europe (Basel, Switzerland), and WordCamp US (Portland, Oregon, USA).

";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:30:"com-wordpress:feed-additions:1";a:1:{s:7:"post-id";a:1:{i:0;a:5:{s:4:"data";s:5:"18205";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:4;a:6:{s:4:"data";s:57:" ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:33:"Write Books With the Block Editor";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:69:"https://wordpress.org/news/2024/12/write-books-with-the-block-editor/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Mon, 16 Dec 2024 08:36:57 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:1:{i:0;a:5:{s:4:"data";s:7:"General";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:35:"https://wordpress.org/news/?p=18176";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:327:"If you need a little push to start writing this winter, in the comfort of your familiar editor, here it is! You can now use the Block Editor to create electronic books and other documents—all completely offline. What a full circle moment for Gutenberg! The Block Editor contains so many features I miss when writing […]";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:4:"Ella";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:6919:"

If you need a little push to start writing this winter, in the comfort of your familiar editor, here it is! You can now use the Block Editor to create electronic books and other documents—all completely offline. What a full circle moment for Gutenberg!

The Block Editor contains so many features I miss when writing in other editors. It produces clean, semantic markup. You can paste in content from anywhere and the editor will clean it up for you, or paste a link onto selected text to auto-link. The List View and Outline panels allow you to easily navigate and inspect the content. And we’re constantly iterating on the Block Editor: more features and improvements are on the way, such as refined drag and drop interactions coming in early 2025.

All this inspired me to wrap our editor in an app that can read and write local files—just as other document editors do. It turns out that EPUB is the best file format to store the content, because EPUB is an open standard for e-books that is essentially a ZIP file containing HTML and media—HTML like your WordPress posts!

And just like that, the WordPress Block Editor can also be used to write books! The cool thing about EPUB files is that any e-book app, such as Kindle and Apple Books, can open it. So even if someone doesn’t have this editor, they can still easily read the content, which makes the files it produces portable.

The editor allows you to create a cover, so you can easily distinguish between the books or documents you write. It will also treat each heading as a chapter so you can easily navigate content when opened in an e-book reader.

The term “book” should be taken broadly. While the file that the Block Editor produces is primarily used for e-books, you can create any document with it. It’s possible to export your document to a DOCX file in case you need it, though the more complex blocks are not supported yet.

It is still very much a nascent project. There’s many features left to be added, such as revisions and the ability to open any externally created EPUB files, or even DOCX files, so keep an eye out for these in the coming weeks and months! If you’re interested in this editor, it’s all open source, and I welcome any kind of help.

For now, the demo editor is installable as a Progressive Web App (PWA) in Chrome. While it’s totally usable without installation, it does give you some nice benefits such as allowing you to open the EPUB files directly from your OS. In the future we might wrap it in proper native apps. Your feedback is welcome on GitHub!

";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:30:"com-wordpress:feed-additions:1";a:1:{s:7:"post-id";a:1:{i:0;a:5:{s:4:"data";s:5:"18176";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}}}i:5;a:6:{s:4:"data";s:57:" ";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";s:5:"child";a:4:{s:0:"";a:6:{s:5:"title";a:1:{i:0;a:5:{s:4:"data";s:36:"Openverse.org: A Sight for Sore Eyes";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"link";a:1:{i:0;a:5:{s:4:"data";s:71:"https://wordpress.org/news/2024/12/openverse-org-a-sight-for-sore-eyes/";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:7:"pubDate";a:1:{i:0;a:5:{s:4:"data";s:31:"Wed, 11 Dec 2024 17:45:50 +0000";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:8:"category";a:1:{i:0;a:5:{s:4:"data";s:7:"General";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:4:"guid";a:1:{i:0;a:5:{s:4:"data";s:35:"https://wordpress.org/news/?p=18168";s:7:"attribs";a:1:{s:0:"";a:1:{s:11:"isPermaLink";s:5:"false";}}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}s:11:"description";a:1:{i:0;a:5:{s:4:"data";s:378:"Openverse.org, the vibrant platform for openly licensed media, has introduced a sleek and modern Dark Mode feature. This new site theme is designed to enhance users’ comfort and style as they explore the extensive library of creative resources. Whether for late-night browsing or simply a preference for darker aesthetics, Dark Mode makes engaging with Openverse […]";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:32:"http://purl.org/dc/elements/1.1/";a:1:{s:7:"creator";a:1:{i:0;a:5:{s:4:"data";s:14:"Brett McSherry";s:7:"attribs";a:0:{}s:8:"xml_base";s:0:"";s:17:"xml_base_explicit";b:0;s:8:"xml_lang";s:0:"";}}}s:40:"http://purl.org/rss/1.0/modules/content/";a:1:{s:7:"encoded";a:1:{i:0;a:5:{s:4:"data";s:7308:"

Openverse.org, the vibrant platform for openly licensed media, has introduced a sleek and modern Dark Mode feature. This new site theme is designed to enhance users’ comfort and style as they explore the extensive library of creative resources. Whether for late-night browsing or simply a preference for darker aesthetics, Dark Mode makes engaging with Openverse easier on the eyes and more personalized than ever.

By reducing screen brightness in low-light settings, Dark Mode offers a more relaxed viewing experience, helping to minimize eye strain. It also caters to users with light sensitivity, creating a more inclusive browsing environment. This thoughtful addition underscores Openverse’s commitment to delivering tools that are as functional as they are visually appealing.

The release of Dark Mode is part of Openverse’s broader effort to innovate and adapt to the needs of its growing community. From the thoughtful interface design to the careful attention to accessibility, every detail was crafted to reflect Openverse’s mission of empowering creativity. By embracing modern frontend implementations like Dark Mode without compromising usability or accessibility, Openverse continues to grow while honoring the brand’s essence. In addition, this update lays the groundwork for future developments aimed at providing even more customization options and improved user experiences.

“Dark Mode marks an exciting step forward for Openverse. We designed and implemented a new user interface that keeps the brand’s essence while providing the same search experience. We’re thrilled to see how this feature fits within users’ preferences and enhances the creative journey.”  – Francisco Vera. Designer

Ready to explore Openverse in a whole new light? Head to Openverse.org today and look for the Dark Mode toggle in the site footer.

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WordPress 6.7.1 is now available!

This minor release features 16 bug fixes throughout Core and the Block Editor.

WordPress 6.7.1 is a fast-follow release with a strict focus on bugs introduced in WordPress 6.7. The next major release will be version 6.8, planned for April 2025.

If you have sites that support automatic background updates, the update process will begin automatically.

You can download WordPress 6.7.1 from WordPress.org, or visit your WordPress Dashboard, click “Updates”, and then click “Update Now”.

For more information on this release, please visit the HelpHub site. You can find a summary of the maintenance updates in this release in the Release Candidate announcement.

Thank you to these WordPress contributors

This release was led by Jonathan Desrosiers and Carlos Bravo.

WordPress 6.7.1 would not have been possible without the contributions of the following people. Their asynchronous coordination to deliver maintenance fixes into a stable release is a testament to the power and capability of the WordPress community.

abcsun, Adam Silverstein, Ahsan Khan, Aki Hamano, Alexander Bigga, Andrew Ozz, Ankit Kumar Shah, Antoine, bluantinoo, Carlos Bravo, Carolina Nymark, charleslf, Christoph Daum, David Smith, dhewercorus, Dhruvang21, Dilip Bheda, dooperweb, Eshaan Dabasiya, Felix Arntz, finntown, Firoz Sabaliya, George Mamadashvili, glynnquelch, Greg Ziółkowski, Himanshu Pathak, jagirbahesh, Jarda Snajdr, Jb Audras, Jeffrey Paul, Joe Dolson, Joe McGill, John Blackbourn, Jonathan Desrosiers, Jon Surrell, Julie Moynat, Julio Potier, laurelfulford, Lee Collings, Lena Morita, luisherranz, Matias Benedetto, Mayank Tripathi, Michal Czaplinski, Miguel Fonseca, miroku, Mukesh Panchal, Narendra Sishodiya, Nik Tsekouras, Oliver Campion, Pascal Birchler, Peter Wilson, ramonopoly, Ravi Gadhiya, Rishi Mehta, room34, Roy Tanck, Ryo, sailpete, Sainath Poojary, Sarthak Nagoshe, Sergey Biryukov, SirLouen, S P Pramodh, Stephen Bernhardt, stimul, Sukhendu Sekhar Guria, TigriWeb, Tim W, tobifjellner (Tor-Bjorn “Tobi” Fjellner), Vania, Yogesh Bhutkar, YoWangdu, Zargarov, and zeelthakkar.

How to contribute

To get involved in WordPress core development, head over to Trac, pick a ticket, and join the conversation in the #core and #6-8-release-leads channels. Need help? Check out the Core Contributor Handbook.

Thanks to @marybaum, @aaroncampbell, @jeffpaul, @audrasjb, @cbravobernal, @ankit-k-gupta for proofreading.

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Each WordPress release celebrates an artist who has made an indelible mark on the world of music. WordPress 6.7, code-named “Rollins,” pays tribute to the legendary jazz saxophonist Sonny Rollins. Known as one of the greatest improvisers and pioneers in jazz, Rollins has influenced generations of musicians with his technical brilliance, innovative spirit, and fearless approach to musical expression.

Sonny Rollins’ work is characterized by its unmatched energy and emotional depth. His compositions, such as “St. Thomas,” “Oleo,” and “Airegin,” are timeless jazz standards, celebrated for their rhythmic complexity and melodic inventiveness. Rollins’ bold and exploratory style resonates with WordPress’ own commitment to empowering creators to push boundaries and explore new possibilities in digital expression.

Embrace the spirit of innovation and spontaneity that defines Rollins’ sound as you dive into the new features and enhancements of WordPress 6.7.

Welcome to WordPress 6.7!

WordPress 6.7 debuts the modern Twenty Twenty-Five theme, offering ultimate design flexibility for any blog at any scale. Control your site typography like never before with new font management features. The new Zoom Out feature lets you design your site with a macro view, stepping back from the details to bring the big picture to life.

Introducing Twenty Twenty-Five

Endless possibility without complexity

Twenty Twenty-Five offers a flexible, design-focused theme that lets you build stunning sites with ease. Tailor your aesthetic with an array of style options, block patterns, and color palettes. Pared down to the essentials, this is a theme that can truly grow with you.

Get the big picture with Zoom Out

Explore your content from a new perspective

Edit and arrange entire sections of your content like never before. A broader view of your site lets you add, edit, shuffle, or remove patterns to your liking. Embrace your inner architect.

Connect blocks and custom fields with no hassle (or code)

A streamlined way to create dynamic content

This feature introduces a new UI for connecting blocks to custom fields, putting control of dynamic content directly in the editor. Link blocks with fields in just a few clicks, enhancing flexibility and efficiency when building. Your clients will love you—as if they didn’t already.

Embrace your inner font nerd

New style section, new possibilities

Create, edit, remove, and apply font size presets with the next addition to the Styles interface. Override theme defaults or create your own custom font size, complete with fluid typography for responsive font scaling. Get into the details!

Performance

WordPress 6.7 delivers important performance updates, including faster pattern loading, optimized previews in the data views component, improved PHP 8+ support and removal of deprecated code, auto sizes for lazy-loaded images, and more efficient tag processing in the HTML API.

Accessibility

65+ accessibility fixes and enhancements focus on foundational aspects of the WordPress experience, from improving user interface components and keyboard navigation in the Editor, to an accessible heading on WordPress login screens and clearer labeling throughout.

And much more

For a comprehensive overview of all the new features and enhancements in WordPress 6.7, please visit the feature-showcase website.

Learn more about WordPress 6.7

Learn WordPress is a free resource for new and experienced WordPress users. Learn is stocked with how-to videos on using various features in WordPress, interactive workshops for exploring topics in-depth, and lesson plans for diving deep into specific areas of WordPress.

Read the WordPress 6.7 Release Notes for information on installation, enhancements, fixed issues, release contributors, learning resources, and the list of file changes.

Explore the WordPress 6.7 Field Guide. Learn about the changes in this release with detailed developer notes to help you build with WordPress.

The 6.7 release squad

Every release comes to you from a dedicated team of enthusiastic contributors who help keep things on track and moving smoothly. The team that has led 6.7 is a cross-functional group of contributors who are always ready to champion ideas, remove blockers, and resolve issues.

Thank you, contributors

The mission of WordPress is to democratize publishing and embody the freedoms that come with open source. A global and diverse community of people collaborating to strengthen the software supports this effort.

WordPress 6.7 reflects the tireless efforts and passion of more than 780 contributors in countries all over the world. This release also welcomed over 230 first-time contributors!

Their collaboration delivered more than 340 enhancements and fixes, ensuring a stable release for all—a testament to the power and capability of the WordPress open source community.

75thtrombone · Aaron Jorbin · Aaron Robertshaw · Aaron Ware · aatanasov · abcsun · Abha Thakor · abhi3315 · Abhishek Deshpande · Abir · acafourek · Adam Heckler · Adam Silverstein · Adam Wood · Adam Zieliński · Adarsh Akshat · Adrian · aduth · Ahmar Zaidi · Ahmed Kabir Chaion · Ahmed Saeed · Ahsan Khan · Ajit Bohra · Akash Dhawade · Aki Hamano · Akira Tachibana · Akshat Kakkad · Al-Amin Firdows · Alan Fuller · Albert Juhé Lluveras · Alessandro Tesoro · Alessio · Alex Concha · Alex Cuadra · Alex Lende · Alex Stine · alex27 · Alexander Bigga · Alexander Frank · Alexandre Buffet · Alexandru Horeanu · Ali Aghdam · Ali Ali · allilevine · Alvaro Gómez · Alvi Tazwar · Amin Charoliya · Amir Abbas · Amit Raj · Amjad Ali · Anand Thakkar · andergmartins · Andrea Fercia · Andrea Roenning · Andrei Draganescu · Andrei Lupu · andreiglingeanu · Andrew Hayward · Andrew Ozz · Andrew Serong · Andrey "Rarst" Savchenko · André Maneiro · Andy Fragen · Angelika Reisiger · Aniket Patel · Ankit K Gupta · Ankit K. Gupta · Ankit Kumar Shah · Ankur Vishwakarma · Anne McCarthy · Anthony Burchell · Anthony Hortin · Antoine · Anton · Antonio Sejas · Anveshika Srivastava · apermo · apmeyer · Ari Stathopoulos · Armando J. Perez Carreno · Armands · arnaudbroes · Art Smith · Artemio Morales · Arthur Chu · arypneta · asafm7 · Aslam Doctor · Autumn · Ayesh Karunaratne · Bård Bjerke Johannessen · Béryl de La Grandière · Balu B · Barry Ceelen · Bart Kalisz · Beatriz Fialho · Ben Dwyer · Benedikt Ledl · Benjamin Denis · Benjamin Zekavica · Benoit Chantre · Bernhard Kau · Bernhard Reiter · Bernhard Riedl · bernhard-reiter · berubenic · Bhavesh Desai · Bijit Deb · Birgit Pauli-Haack · blindmikey · bluantinoo · bobbyleenoblestudios · Bogdan Nikolic · Brad · brad hogan · Brad Jorsch · Brandon Kraft · Brent Jett · Brett Shumaker · Brian Alexander · Brian Coords · Brian Gardner · Brian Gosnell · Brian Henry · bridgetwes · brobken · Bruno Freiberger Garcia · Cambabutonono · Carlos Bravo · Carlos G. 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Istiaq Hossain · mdviralsampat · megane9988 · Mehedi Hassan · Mehul Kaklotar · Mel Choyce-Dwan · meteorlxy · Micha Krapp · Michael · Michael Beckwith · Michael Bourne · Michael James Ilett · michaelpick · Michal Czaplinski · Michelle Bulloch · Miguel Axcar · Miguel Fonseca · Miguel Lezama · Mikael Korpela · Mike McAlister · Mike Poland · Mike Ritter · mikeb8s · Mikey Binns · milamj · Milana Cap · miroku · Mitchell Austin · mklusak · mleathem · mlf20 · Mobarak Ali · Mohit Dadhich · Morgan Estes · Moses Cursor Ssebunya · Mosne / Paolo Tesei · mossy2100 · mreishus · Muhibul Haque · mujuonly · Mukesh Panchal · Mumtahina Faguni · Nadir Seghir a11n · Naeem Haque · Nagesh Pai · Narendra Sishodiya · Naresh Bheda · Nate Finch · Nate Gay · Nazmul Hasan Robin · Nebojša Jurčić · nek285 · nendeb · neo2k23 · neotrope · Nicholas Garofalo · Nick Bohle · Nick Diego · Nick Halsey · Nick the Geek · Nicole Furlan · nidhidhandhukiya · Nihar Ranjan Das · Nik Tsekouras · Nikita Solanki · Niraj Giri · Nirav Sherasiya · Nithin John · Nithin SreeRaj · Noah Allen · Noruzzaman · nurielmeni · obliviousharmony · Olaf Lederer · Olga Gleckler · Oliver Campion · Olivier Lafleur · Omar Alshaker · Oscar Hugo Paz · p15h · Paal Joachim Romdahl · Pablo Hernández · Pablo Honey · Pamela Ribeiro · pander · Paolo L. 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More than 40 locales have fully translated WordPress 6.7 into their language making this one of the most translated releases ever on day one. Community translators are working hard to ensure more translations are on their way. Thank you to everyone who helps make WordPress available in 200 languages.

Last but not least, thanks to the volunteers who contribute to the support forums by answering questions from WordPress users worldwide.

Get involved

Participation in WordPress goes far beyond coding, and learning more and getting involved is easy. Discover the teams that come together to Make WordPress and use this interactive tool to help you decide which is right for you.

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The third release candidate (RC3) for WordPress 6.7 is ready for download and testing!

This version of the WordPress software is under development. Please do not install, run, or test this version of WordPress on production or mission-critical websites. Instead, it’s recommended that you evaluate RC3 on a test server and site.

Reaching this phase of the release cycle is an important milestone. While release candidates are considered ready for release, testing remains crucial to ensure that everything in WordPress 6.7 is the best it can be.

You can test WordPress 6.7 RC3 in four ways:

PluginInstall and activate the WordPress Beta Tester plugin on a WordPress install. (Select the “Bleeding edge” channel and “Beta/RC Only” stream).
Direct DownloadDownload the RC3 version (zip) and install it on a WordPress website.
Command LineUse the following WP-CLI command:
wp core update --version=6.7-RC3
WordPress PlaygroundUse the 6.7 RC3 WordPress Playground instance (available within 35 minutes after the release is ready) to test the software directly in your browser without the need for a separate site or setup.
You can test the RC3 version in four ways.

The current target for the WordPress 6.7 release is November 12, 2024. Get an overview of the 6.7 release cycle, and check the Make WordPress Core blog for 6.7-related posts in the coming weeks for further details.

What’s in WordPress 6.7 RC3?

Get a recap of WordPress 6.7’s highlighted features in the Beta 1 announcement. For more technical information related to issues addressed since RC2, you can browse the following links:

How you can contribute

WordPress is open source software made possible by a passionate community of people collaborating on and contributing to its development. The resources below outline various ways you can help the world’s most popular open source web platform, regardless of your technical expertise.

Get involved in testing

Testing for issues is critical to ensuring WordPress is performant and stable. It’s also a meaningful way for anyone to contribute. This detailed guide will walk you through testing features in WordPress 6.7. For those new to testing, follow this general testing guide for more details on getting set up.

If you encounter an issue, please report it to the Alpha/Beta area of the support forums or directly to WordPress Trac if you are comfortable writing a reproducible bug report. You can also check your issue against a list of known bugs.

Curious about testing releases in general? Follow along with the testing initiatives in Make Core and join the #core-test channel on Making WordPress Slack.

Search for vulnerabilities

From now until the final release of WordPress 6.7 (scheduled for November 12, 2024), the monetary reward for reporting new, unreleased security vulnerabilities is doubled. Please follow responsible disclosure practices as detailed in the project’s security practices and policies outlined on the HackerOne page and in the security white paper.

Update your theme or plugin

For plugin and theme authors, your products play an integral role in extending the functionality and value of WordPress for all users. 

Thanks for continuing to test your themes and plugins with the WordPress 6.7 beta releases. With RC3, you’ll want to conclude your testing and update the “Tested up to” version in your plugin’s readme file to 6.7.

If you find compatibility issues, please post detailed information to the support forum.

Help translate WordPress

Do you speak a language other than English? ¿Español? Français? Русский? 日本? हिन्दी? বাংলা? You can help translate WordPress into more than 100 languages.

Release the haiku

RC3 arrives,
Final polish, last bugs fall,
Six point seven calls.

Thank you to the following contributors for collaborating on this post: @peterwilsoncc, @joedolson, @sabernhardt.

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The second release candidate (RC2) for WordPress 6.7 is ready for download and testing!

This version of the WordPress software is under development. Please do not install, run, or test this version of WordPress on production or mission-critical websites. Instead, it’s recommended that you evaluate RC2 on a test server and site.

Reaching this phase of the release cycle is an important milestone. While release candidates are considered ready for release, testing remains crucial to ensure that everything in WordPress 6.7 is the best it can be.

You can test WordPress 6.7 RC2 in four ways:

PluginInstall and activate the WordPress Beta Tester plugin on a WordPress install. (Select the “Bleeding edge” channel and “Beta/RC Only” stream).
Direct DownloadDownload the RC2 version (zip) and install it on a WordPress website.
Command LineUse the following WP-CLI command:
wp core update --version=6.7-RC2
WordPress PlaygroundUse the 6.7 RC2 WordPress Playground instance (available within 35 minutes after the release is ready) to test the software directly in your browser without the need for a separate site or setup.
You can test the RC2 version in four ways.

The current target for the WordPress 6.7 release is November 12, 2024. Get an overview of the 6.7 release cycle, and check the Make WordPress Core blog for 6.7-related posts in the coming weeks for further details.

What’s in WordPress 6.7 RC2?

Get a recap of WordPress 6.7’s highlighted features in the Beta 1 announcement. For more technical information related to issues addressed since RC1, you can browse the following links:

How you can contribute

WordPress is open source software made possible by a passionate community of people collaborating on and contributing to its development. The resources below outline various ways you can help the world’s most popular open source web platform, regardless of your technical expertise.

Get involved in testing

Testing for issues is critical to ensuring WordPress is performant and stable. It’s also a meaningful way for anyone to contribute. This detailed guide will walk you through testing features in WordPress 6.7. For those new to testing, follow this general testing guide for more details on getting set up.

If you encounter an issue, please report it to the Alpha/Beta area of the support forums or directly to WordPress Trac if you are comfortable writing a reproducible bug report. You can also check your issue against a list of known bugs.

Curious about testing releases in general? Follow along with the testing initiatives in Make Core and join the #core-test channel on Making WordPress Slack.

Search for vulnerabilities

From now until the final release of WordPress 6.7 (scheduled for November 12, 2024), the monetary reward for reporting new, unreleased security vulnerabilities is doubled. Please follow responsible disclosure practices as detailed in the project’s security practices and policies outlined on the HackerOne page and in the security white paper.

Update your theme or plugin

For plugin and theme authors, your products play an integral role in extending the functionality and value of WordPress for all users. 

Thanks for continuing to test your themes and plugins with the WordPress 6.7 beta releases. With RC2, you’ll want to conclude your testing and update the “Tested up to” version in your plugin’s readme file to 6.7.

If you find compatibility issues, please post detailed information to the support forum.

Help translate WordPress

Do you speak a language other than English? ¿Español? Français? Русский? 日本? हिन्दी? বাংলা? You can help translate WordPress into more than 100 languages.

Release the haiku

Six point seven’s dawn,
RC2 sweeps bugs away,
Sites stand firm and strong.

Thank you to the following contributors for collaborating on this post: @jorbin.

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Noémie Robil; Pierre de la Grange; Ivan Moszer; Isabelle Le Ber; Olivier Colliot; Emmanuelle Becker; The PREV-DEMALS study group '), (1709, 1748, '_authors', 'field_5ff868da50eec'), (1710, 1748, 'date', '20201125'), (1711, 1748, '_date', 'field_5ff868f750eed'), (1712, 1748, 'journal', 'Journal of Neurology, Neurosurgery and Psychiatry'), (1713, 1748, '_journal', 'field_5ff8692750eee'), (1714, 1748, 'keywords', 'a:4:{i:0;s:2:"22";i:1;s:2:"23";i:2;s:2:"24";i:3;s:2:"25";}'), (1715, 1748, '_keywords', 'field_5ff8695650eef'), (1716, 1748, 'link', 'http://dx.doi.org/10.1136/jnnp-2020-324647'), (1717, 1748, '_link', 'field_5ff8696250ef0'), (1718, 1748, 'abstract_text', 'Objective: To identify potential biomarkers of preclinical and clinical progression in chromosome 9 open reading frame 72 gene (C9orf72)-associated disease by assessing the expression levels of plasma microRNAs (miRNAs) in C9orf72 patients and presymptomatic carriers. Methods: The PREV-DEMALS study is a prospective study including 22 C9orf72 patients, 45 presymptomatic C9orf72mutation carriers and 43 controls. We assessed the expression levels of 2576 miRNAs, among which 589 were above noise level, in plasma samples of all participants using RNA sequencing. The expression levels of the differentially expressed miRNAs between patients, presymptomatic carriers and controls were further used to build logistic regression classifiers. Results: Four miRNAs were differentially expressed between patients and controls: miR-34a-5p and miR-345-5p were overexpressed, while miR-200c-3p and miR-10a-3p were underexpressed in patients. MiR-34a-5p was also overexpressed in presymptomatic carriers compared with healthy controls, suggesting that miR-34a-5p expression is deregulated in cases with C9orf72 mutation. Moreover, miR-345-5p was also overexpressed in patients compared with presymptomatic carriers, which supports the correlation of miR-345-5p expression with the progression of C9orf72-associated disease. Together, miR-200c-3p and miR-10a-3p underexpression might be associated with full-blown disease. Four presymptomatic subjects in transitional/prodromal stage, close to the disease conversion, exhibited a stronger similarity with the expression levels of patients. Conclusions: We identified a signature of four miRNAs differentially expressed in plasma between clinical conditions that have potential to represent progression biomarkers for C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis. This study suggests that dysregulation of miRNAs is dynamically altered throughout neurodegenerative diseases progression, and can be detectable even long before clinical onset.'), (1719, 1748, '_abstract_text', 'field_5ff869ab50ef3'), (1720, 1748, 'description_text', 'Description is here'), (1721, 1748, '_description_text', 'field_5ff869cb50ef5'), (1722, 1748, '_wp_old_slug', 'plasma-microrna-signature-in-presymptomatic-and-symptomatic-subjects-with-c9orf72-associated-frontotemporal-dementia-and-amyotrophic-lateral-sclerosis'), (1731, 1748, 'abstract', 'Objective: To identify potential biomarkers of preclinical and clinical progression in chromosome 9 open reading frame 72 gene (C9orf72)-associated disease by assessing the expression levels of plasma microRNAs (miRNAs) in C9orf72patients and presymptomatic carriers. Methods: The PREV-DEMALS study is a prospective study including 22 C9orf72 patients, 45 presymptomatic C9orf72 mutation carriers and 43 controls. We assessed the expression levels of 2576 miRNAs, among which 589 were above noise level, in plasma samples of all participants using RNA sequencing. The expression levels of the differentially expressed miRNAs between patients, presymptomatic carriers and controls were further used to build logistic regression classifiers. Results: Four miRNAs were differentially expressed between patients and controls: miR-34a-5p and miR-345-5p were overexpressed, while miR-200c-3p and miR-10a-3p were underexpressed in patients. MiR-34a-5p was also overexpressed in presymptomatic carriers compared with healthy controls, suggesting that miR-34a-5p expression is deregulated in cases with C9orf72 mutation. Moreover, miR-345-5p was also overexpressed in patients compared with presymptomatic carriers, which supports the correlation of miR-345-5p expression with the progression of C9orf72-associated disease. Together, miR-200c-3p and miR-10a-3p underexpression might be associated with full-blown disease. Four presymptomatic subjects in transitional/prodromal stage, close to the disease conversion, exhibited a stronger similarity with the expression levels of patients. Conclusions: We identified a signature of four miRNAs differentially expressed in plasma between clinical conditions that have potential to represent progression biomarkers for C9orf72-associated frontotemporal dementia and amyotrophic lateral sclerosis. This study suggests that dysregulation of miRNAs is dynamically altered throughout neurodegenerative diseases progression, and can be detectable even long before clinical onset.'), (1732, 1748, '_abstract', 'field_5ff869ab50ef3'), (1733, 1748, 'description', 'Frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) are devastating neurodegenerative diseases. They may have a common genetic cause, the most typical being a mutation in the C9orf72 gene. Since there is no cure so far, it is essential to identify biomarkers of disease progression that could be used to evaluate the efficacy of clinical trials. The goal of this study was to assess the use of microRNAs (small molecules that regulate gene expression) found in blood plasma as progression biomarkers of FTD/ALS. We analysed blood samples from 115 subjects (FTD/ALS patients, presymptomatic mutation carriers and healthy controls) and identified four differentially expressed microRNAs. We then built models based on the expression levels of this microRNA signature, which were able to predict if a new sample came from a patient, a presymptomatic carrier or a healthy control. Our results highlight the potential of plasma microRNAs as progression biomarkers for FTD/ALS, which could provide a non-invasive method to monitor new disease-modifying therapies.'), (1734, 1748, '_description', 'field_5ff869cb50ef5'), (1738, 1778, '_edit_lock', '1645202621:10'), (1739, 1778, '_edit_last', '8'), (1740, 1778, '_wp_page_template', 'latest-publications.php'), (1741, 1782, '_menu_item_type', 'post_type'), (1742, 1782, '_menu_item_menu_item_parent', '1764'), (1743, 1782, '_menu_item_object_id', '1778'), (1744, 1782, '_menu_item_object', 'page'), (1745, 1782, '_menu_item_target', ''), (1746, 1782, '_menu_item_classes', 'a:1:{i:0;s:0:"";}'), (1747, 1782, '_menu_item_xfn', ''), (1748, 1782, '_menu_item_url', ''), (1752, 1791, '_wp_attached_file', '2021/01/Figure-1-Boxplots.png'), (1753, 1791, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:1309;s:6:"height";i:1025;s:4:"file";s:29:"2021/01/Figure-1-Boxplots.png";s:5:"sizes";a:4:{s:6:"medium";a:4:{s:4:"file";s:29:"Figure-1-Boxplots-300x235.png";s:5:"width";i:300;s:6:"height";i:235;s:9:"mime-type";s:9:"image/png";}s:5:"large";a:4:{s:4:"file";s:30:"Figure-1-Boxplots-1024x802.png";s:5:"width";i:1024;s:6:"height";i:802;s:9:"mime-type";s:9:"image/png";}s:9:"thumbnail";a:4:{s:4:"file";s:29:"Figure-1-Boxplots-150x150.png";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:9:"image/png";}s:12:"medium_large";a:4:{s:4:"file";s:29:"Figure-1-Boxplots-768x601.png";s:5:"width";i:768;s:6:"height";i:601;s:9:"mime-type";s:9:"image/png";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'), (1754, 1748, 'Illustration', '1791'), (1755, 1748, '_Illustration', 'field_5ffcb3a32211f'), (1756, 1748, 'publisher_link', 'http://dx.doi.org/10.1136/jnnp-2020-324647'), (1757, 1748, '_publisher_link', 'field_5ff8696250ef0'), (1758, 1748, 'open_access_link', ''), (1759, 1748, '_open_access_link', 'field_5ffca31d9e5f6'), (1778, 1748, 'image', '1791'), (1779, 1748, '_image', 'field_5ffcb3a32211f'), (1781, 1797, '_edit_lock', '1618908385:8'), (1782, 1797, '_edit_last', '11'), (1784, 1799, '_wp_attached_file', '2021/01/Figure_architectures-scaled.jpg'), (1785, 1799, '_wp_attachment_metadata', 'a:6:{s:5:"width";i:2560;s:6:"height";i:1541;s:4:"file";s:39:"2021/01/Figure_architectures-scaled.jpg";s:5:"sizes";a:6:{s:6:"medium";a:4:{s:4:"file";s:32:"Figure_architectures-300x181.jpg";s:5:"width";i:300;s:6:"height";i:181;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:33:"Figure_architectures-1024x616.jpg";s:5:"width";i:1024;s:6:"height";i:616;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:32:"Figure_architectures-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:32:"Figure_architectures-768x462.jpg";s:5:"width";i:768;s:6:"height";i:462;s:9:"mime-type";s:10:"image/jpeg";}s:9:"1536x1536";a:4:{s:4:"file";s:33:"Figure_architectures-1536x925.jpg";s:5:"width";i:1536;s:6:"height";i:925;s:9:"mime-type";s:10:"image/jpeg";}s:9:"2048x2048";a:4:{s:4:"file";s:34:"Figure_architectures-2048x1233.jpg";s:5:"width";i:2048;s:6:"height";i:1233;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"1";s:8:"keywords";a:0:{}}s:14:"original_image";s:24:"Figure_architectures.jpg";}'), (1786, 1797, 'title', 'Deep learning for brain disorders: from data processing to disease treatment'), (1787, 1797, '_title', 'field_5ff86849c7493'), (1788, 1797, 'authors', 'Ninon Burgos; Simona Bottani; Johann Faouzi; Elina Thibeau-Sutre; Olivier Colliot'), (1789, 1797, '_authors', 'field_5ff868da50eec'), (1790, 1797, 'date', '20201215'), (1791, 1797, '_date', 'field_5ff868f750eed'), (1792, 1797, 'journal', 'Briefings in Bioinformatics'), (1793, 1797, '_journal', 'field_5ff8692750eee'), (1794, 1797, 'keywords', 'a:4:{i:0;s:2:"26";i:1;s:2:"27";i:2;s:2:"28";i:3;s:2:"29";}'), (1795, 1797, '_keywords', 'field_5ff8695650eef'), (1796, 1797, 'publisher_link', 'https://doi.org/10.1093/bib/bbaa310'), (1797, 1797, '_publisher_link', 'field_5ff8696250ef0'), (1798, 1797, 'open_access_link', 'https://hal.inria.fr/hal-03070554'), (1799, 1797, '_open_access_link', 'field_5ffca31d9e5f6'), (1800, 1797, 'image', '1799'), (1801, 1797, '_image', 'field_5ffcb3a32211f'), (1802, 1797, 'abstract', 'In order to reach precision medicine and improve patients’ quality of life, machine learning is increasingly used in medicine. Brain disorders are often complex and heterogeneous, and several modalities such as demographic, clinical, imaging, genetics and environmental data have been studied to improve their understanding. Deep learning, a subpart of machine learning, provides complex algorithms that can learn from such various data. It has become state-of-the-art in numerous fields including computer vision and natural language processing, and is also growingly applied in medicine. In this article, we review the use of deep learning for brain disorders. More specifically, we identify the main applications, the concerned disorders and the types of architectures and data used. Finally, we provide guidelines to bridge the gap between research studies and clinical routine.

Summary key points

'), (1803, 1797, '_abstract', 'field_5ff869ab50ef3'), (1804, 1797, 'description', 'This review will enable readers to grasp the full potential of deep learning for brain disorders as it presents the main uses of deep learning all along the medical data analysis chain: from data acquisition to disease treatment. We first focus on data processing, covering image reconstruction, signal enhancement and cross-modality image synthesis, and on the biomarkers that can be extracted from spatio-temporal neuroimaging data, such as the volume of normal structures or of lesions. We then describe how deep learning can be used to detect diseases, predict their evolution, improve their understanding and help develop treatments. For these applications, we emphasize the types of architectures and data used, as well as the concerned disorders. Finally, we highlight trending applications and provide guidelines to bridge the gap between research studies and clinical routine.'), (1805, 1797, '_description', 'field_5ff869cb50ef5'), (1809, 1748, 'caption', 'This is the description of the image!'), (1810, 1748, '_caption', 'field_5ffd5f2b99c98'), (1811, 1797, 'caption', 'Common deep learning architectures for brain disorders. a) U-Net is the most popular architecture for biomedical image segmentation. U-Net architectures have also been used for image reconstruction and synthesis. b) Autoencoders have been used for disease detection, prediction of treatment and integration of multimodal data. c) Variational autoencoders have been used for image segmentation, disease detection and disease subtyping. d) Generative adversarial networks can be used for data augmentation. e) Conditional generative adversarial networks have been used for signal enhancement, image synthesis and disease prediction.'), (1812, 1797, '_caption', 'field_5ffd5f2b99c98'), (1841, 1803, '_edit_lock', '1610724218:8'), (1842, 1804, '_wp_attached_file', '2021/01/Image_1_A-Reliable-and-Rapid-Language-Tool-for-the-Diagnosis-Classification-and-Follow-Up-of-Primary-Progressive-Aphasia-Variants.tif'), (1843, 1803, '_edit_last', '8'), (1844, 1803, 'title', 'A Reliable and Rapid Language Tool for the Diagnosis, Classification, and Follow-Up of Primary Progressive Aphasia Variants'), (1845, 1803, '_title', 'field_5ff86849c7493'), (1846, 1803, 'authors', 'Stéphane Epelbaum, Yasmina Michel Saade, Constance Flamand Roze, Emmanuel Roze, Sophie Ferrieux, Céline Arbizu, Marie Nogues, Carole Azuar,Bruno Dubois, Sophie Tezenas du Montcel and Marc Teichmann'), (1847, 1803, '_authors', 'field_5ff868da50eec'), (1848, 1803, 'date', '20210105'), (1849, 1803, '_date', 'field_5ff868f750eed'), (1850, 1803, 'journal', 'Frontiers in Neurology '), (1851, 1803, '_journal', 'field_5ff8692750eee'), (1852, 1803, 'keywords', 'a:3:{i:0;s:2:"30";i:1;s:2:"27";i:2;s:2:"31";}'), (1853, 1803, '_keywords', 'field_5ff8695650eef'), (1854, 1803, 'publisher_link', 'https://www.frontiersin.org/articles/10.3389/fneur.2020.571657/full'), (1855, 1803, '_publisher_link', 'field_5ff8696250ef0'), (1856, 1803, 'open_access_link', 'https://hal.archives-ouvertes.fr/hal-03096896/document'), (1857, 1803, '_open_access_link', 'field_5ffca31d9e5f6'), (1858, 1803, 'image', '1805'), (1859, 1803, '_image', 'field_5ffcb3a32211f'), (1860, 1803, 'caption', 'PARIS-a-reliable-and-Rapid-Language-Tool-for-the-Diagnosis-Classification-and-Follow-Up-of-Primary-Progressive-Aphasia-Variants'), (1861, 1803, '_caption', 'field_5ffd5f2b99c98'), (1862, 1803, 'abstract', '

Background: Primary progressive aphasias (PPA) have been investigated by clinical, therapeutic, and fundamental research but examiner-consistent language tests for reliable reproducible diagnosis and follow-up are lacking.

Methods: We developed and evaluated a rapid language test for PPA (“PARIS”) assessing its inter-examiner consistency, its power to detect and classify PPA, and its capacity to identify language decline after a follow-up of 9 months. To explore the reliability and specificity/sensitivity of the test it was applied to PPA patients (N = 36), typical amnesic Alzheimer\'s disease (AD) patients (N = 24) and healthy controls (N = 35), while comparing it to two rapid examiner-consistent language tests used in stroke-induced aphasia (“LAST”, “ART”).

Results: The application duration of the “PARIS” was ~10 min and its inter-rater consistency was of 88%. The three tests distinguished healthy controls from AD and PPA patients but only the “PARIS” reliably separated PPA from AD and allowed for classifying the two most frequent PPA variants: semantic and logopenic PPA. Compared to the “LAST” and “ART,” the “PARIS” also had the highest sensitivity for detecting language decline.

Conclusions: The “PARIS” is an efficient, rapid, and highly examiner-consistent language test for the diagnosis, classification, and follow-up of frequent PPA variants. It might also be a valuable tool for providing end-points in future therapeutic trials on PPA and other neurodegenerative diseases affecting language processing.

'), (1863, 1803, '_abstract', 'field_5ff869ab50ef3'), (1864, 1803, 'description', 'We designed a simple and robust neurocognitive test to screen for primary progressive aphasia (PPA), a rare neurodegenerative syndrome, to help the clinicians in distinguishing PPA from the more common Alzheimer\'s disease.'), (1865, 1803, '_description', 'field_5ff869cb50ef5'), (1866, 1805, '_wp_attached_file', '2021/01/the-PARIS-scale.jpg'), (1867, 1805, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:1280;s:6:"height";i:720;s:4:"file";s:27:"2021/01/the-PARIS-scale.jpg";s:5:"sizes";a:4:{s:6:"medium";a:4:{s:4:"file";s:27:"the-PARIS-scale-300x169.jpg";s:5:"width";i:300;s:6:"height";i:169;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:28:"the-PARIS-scale-1024x576.jpg";s:5:"width";i:1024;s:6:"height";i:576;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:27:"the-PARIS-scale-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:27:"the-PARIS-scale-768x432.jpg";s:5:"width";i:768;s:6:"height";i:432;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'), (1868, 1803, '_wp_old_slug', '1803'), (1870, 1807, '_edit_lock', '1610813690:11'), (1871, 1807, '_edit_last', '11'), (1872, 1807, 'title', 'Awareness of cognitive decline trajectories in asymptomatic individuals at risk for AD'), (1873, 1807, '_title', 'field_5ff86849c7493'), (1874, 1807, 'authors', 'Federica Cacciamani, Luisa Sambati, Marion Houot, Marie-Odile Habert, Bruno Dubois, Stéphane Epelbaum, on behalf of the INSIGHT-PreAD study group'), (1875, 1807, '_authors', 'field_5ff868da50eec'), (1876, 1807, 'date', '20201014'), (1877, 1807, '_date', 'field_5ff868f750eed'), (1878, 1807, 'journal', 'Alzheimer\'s Research & Therapy '), (1879, 1807, '_journal', 'field_5ff8692750eee'), (1880, 1807, 'keywords', 'a:3:{i:0;s:2:"30";i:1;s:2:"32";i:2;s:2:"33";}'), (1881, 1807, '_keywords', 'field_5ff8695650eef'), (1882, 1807, 'publisher_link', 'https://alzres.biomedcentral.com/articles/10.1186/s13195-020-00700-8'), (1883, 1807, '_publisher_link', 'field_5ff8696250ef0'), (1884, 1807, 'open_access_link', 'https://alzres.biomedcentral.com/articles/10.1186/s13195-020-00700-8'), (1885, 1807, '_open_access_link', 'field_5ffca31d9e5f6'), (1886, 1807, 'image', '1808'), (1887, 1807, '_image', 'field_5ffcb3a32211f'), (1888, 1807, 'caption', ''), (1889, 1807, '_caption', 'field_5ffd5f2b99c98'), (1890, 1807, 'abstract', '

Background

Lack of awareness of cognitive decline (ACD) is common in late-stage Alzheimer’s disease (AD). Recent studies showed that ACD can also be reduced in the early stages.

Methods

We described different trends of evolution of ACD over 3 years in a cohort of memory-complainers and their association to amyloid burden and brain metabolism. We studied the impact of ACD at baseline on cognitive scores’ evolution and the association between longitudinal changes in ACD and in cognitive score.

Results

76.8% of subjects constantly had an accurate ACD (reference class). 18.95% showed a steadily heightened ACD and were comparable to those with accurate ACD in terms of demographic characteristics and AD biomarkers. 4.25% constantly showed low ACD, had significantly higher amyloid burden than the reference class, and were mostly men. We found no overall effect of baseline ACD on cognitive scores’ evolution and no association between longitudinal changes in ACD and in cognitive scores.

Conclusions

ACD begins to decrease during the preclinical phase in a group of individuals, who are of great interest and need to be further characterized.'), (1891, 1807, '_abstract', 'field_5ff869ab50ef3'), (1892, 1807, 'description', 'With several drugs currently being tested, a very early diagnosis of Alzheimer\'s disease becomes even more important. But how to do that? For years it has been thought that older adults who perceive a decline and complain about their memory should be monitored because they may be developing Alzheimer\'s dementia. But in fact, most older adults complain about their memory, as memory changes are normal with aging. We have bucked the trend by using an advanced method of data analysis and discovering that: '), (1893, 1807, '_description', 'field_5ff869cb50ef5'), (1894, 1808, '_wp_attached_file', '2021/01/122096318_954861568341908_4699321636902992732_o.jpg'), (1895, 1808, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:2048;s:6:"height";i:1152;s:4:"file";s:59:"2021/01/122096318_954861568341908_4699321636902992732_o.jpg";s:5:"sizes";a:5:{s:6:"medium";a:4:{s:4:"file";s:59:"122096318_954861568341908_4699321636902992732_o-300x169.jpg";s:5:"width";i:300;s:6:"height";i:169;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:60:"122096318_954861568341908_4699321636902992732_o-1024x576.jpg";s:5:"width";i:1024;s:6:"height";i:576;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:59:"122096318_954861568341908_4699321636902992732_o-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:59:"122096318_954861568341908_4699321636902992732_o-768x432.jpg";s:5:"width";i:768;s:6:"height";i:432;s:9:"mime-type";s:10:"image/jpeg";}s:9:"1536x1536";a:4:{s:4:"file";s:60:"122096318_954861568341908_4699321636902992732_o-1536x864.jpg";s:5:"width";i:1536;s:6:"height";i:864;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'), (1896, 1809, '_wp_attached_file', '2021/02/postdoc_EN-v1.pdf'), (1897, 1809, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:21:"postdoc_EN-v1-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:29:"postdoc_EN-v1-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:30:"postdoc_EN-v1-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:29:"postdoc_EN-v1-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'), (1898, 1809, '_edit_lock', '1612285609:1'), (1899, 1811, '_wp_attached_file', '2021/02/clinica_image_analysis-v2.pdf'), (1900, 1811, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:33:"clinica_image_analysis-v2-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:41:"clinica_image_analysis-v2-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:42:"clinica_image_analysis-v2-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:41:"clinica_image_analysis-v2-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'), (1901, 1811, '_edit_lock', '1612291530:1'), (1902, 1813, '_edit_lock', '1612526526:11'), (1903, 1813, '_edit_last', '11'), (1904, 1813, 'title', 'The ethics of innovation for Alzheimer\'s disease: the risk of overstating evidence for metabolic enhancement protocols'), (1905, 1813, '_title', 'field_5ff86849c7493'), (1906, 1813, 'authors', ' Timothy Daly, Ignacio Mastroleo, David Gorski, Stéphane Epelbaum '), (1907, 1813, '_authors', 'field_5ff868da50eec'), (1908, 1813, 'date', '20210118'), (1909, 1813, '_date', 'field_5ff868f750eed'), (1910, 1813, 'journal', 'Theoretical Medicine and Bioethics (ISSN : 1386-7415, ESSN : 1573-1200)'), (1911, 1813, '_journal', 'field_5ff8692750eee'), (1912, 1813, 'keywords', 'a:7:{i:0;s:2:"34";i:1;s:2:"30";i:2;s:2:"36";i:3;s:2:"35";i:4;s:2:"37";i:5;s:2:"39";i:6;s:2:"38";}'), (1913, 1813, '_keywords', 'field_5ff8695650eef'), (1914, 1813, 'publisher_link', 'https://link.springer.com/article/10.1007/s11017-020-09536-7'), (1915, 1813, '_publisher_link', 'field_5ff8696250ef0'), (1916, 1813, 'open_access_link', 'https://hal.archives-ouvertes.fr/hal-03114575/document'), (1917, 1813, '_open_access_link', 'field_5ffca31d9e5f6'), (1918, 1813, 'image', '1814'), (1919, 1813, '_image', 'field_5ffcb3a32211f'), (1920, 1813, 'caption', 'Innovation and Alzheimer. Friend or foe ?'), (1921, 1813, '_caption', 'field_5ffd5f2b99c98'), (1922, 1813, 'abstract', 'Medical practice is ideally based on robust, relevant research. However, the lack of diseasemodifying treatments for Alzheimer\'s disease has motivated "innovative practice" to improve patients\' well-being despite insufficient evidence for the regular use of such interventions in health systems treating millions of patients. Innovative or new non-validated practice poses at least three distinct ethical questions: first, about the responsible application of new non-validated practice to individual patients (clinical ethics); second, about the way in which data from new non-validated practice are communicated via the scientific and lay press (scientific communication ethics); and third, about the prospect of making new non-validated interventions widely available before more definitive testing (public health ethics). We argue that the authors of metabolic enhancement protocols for Alzheimer\'s disease have overstated the evidence in favor of these interventions within the scientific and lay press, failing to communicate weaknesses in their data and uncertainty about their conclusions. Such unmeasured language may create false hope, cause financial harm, undermine informed consent, and frustrate the production of generalizable knowledge necessary to face the societal problems posed by this devastating disease. We therefore offer more stringent guidelines for responsible innovation in the treatment of Alzheimer\'s disease.'), (1923, 1813, '_abstract', 'field_5ff869ab50ef3'), (1924, 1813, 'description', 'In this article we describe the interplay between a chronic disease with severe therapeutic unmet need such as Alzheimer and innovation. Caveats and ethical guidelines are discussed herein.'), (1925, 1813, '_description', 'field_5ff869cb50ef5'), (1926, 1814, '_wp_attached_file', '2021/02/index.jpg'), (1927, 1814, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:331;s:6:"height";i:152;s:4:"file";s:17:"2021/02/index.jpg";s:5:"sizes";a:2:{s:6:"medium";a:4:{s:4:"file";s:17:"index-300x138.jpg";s:5:"width";i:300;s:6:"height";i:138;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:17:"index-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'), (1934, 1818, '_wp_attached_file', '2021/02/these_interpretability_EN-v1.pdf'), (1935, 1818, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:36:"these_interpretability_EN-v1-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:44:"these_interpretability_EN-v1-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:45:"these_interpretability_EN-v1-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:44:"these_interpretability_EN-v1-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'), (1939, 1818, '_edit_lock', '1612947193:1'), (1941, 1821, '_edit_lock', '1613549278:11'), (1942, 1822, '_wp_attached_file', '2021/02/9783030597092.jpg'), (1943, 1822, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:153;s:6:"height";i:232;s:4:"file";s:25:"2021/02/9783030597092.jpg";s:5:"sizes";a:1:{s:9:"thumbnail";a:4:{s:4:"file";s:25:"9783030597092-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'), (1944, 1822, '_wp_attachment_image_alt', 'Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I - Machine learning'), (1945, 1821, '_edit_last', '11'), (1946, 1821, 'title', 'Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I - machine learning methodologies'), (1947, 1821, '_title', 'field_5ff86849c7493'), (1948, 1821, 'authors', 'Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., Joskowicz, L. (Eds.)'), (1949, 1821, '_authors', 'field_5ff868da50eec'), (1950, 1821, 'date', '20201004'), (1951, 1821, '_date', 'field_5ff868f750eed'), (1952, 1821, 'journal', 'Springer, LNCS 12261'), (1953, 1821, '_journal', 'field_5ff8692750eee'), (1954, 1821, 'keywords', 'a:2:{i:0;s:2:"26";i:1;s:2:"28";}'), (1955, 1821, '_keywords', 'field_5ff8695650eef'), (1956, 1821, 'publisher_link', 'https://www.springer.com/gp/book/9783030597092'), (1957, 1821, '_publisher_link', 'field_5ff8696250ef0'), (1958, 1821, 'open_access_link', ''), (1959, 1821, '_open_access_link', 'field_5ffca31d9e5f6'), (1960, 1821, 'image', '1822'), (1961, 1821, '_image', 'field_5ffcb3a32211f'), (1962, 1821, 'caption', 'MICCAI 2020 - Proceedings (Part I)'), (1963, 1821, '_caption', 'field_5ffd5f2b99c98'), (1964, 1821, 'abstract', 'The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process.'), (1965, 1821, '_abstract', 'field_5ff869ab50ef3'), (1966, 1821, 'description', 'The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography'), (1967, 1821, '_description', 'field_5ff869cb50ef5'), (1968, 1824, '_edit_lock', '1613549743:11'), (1969, 1824, '_edit_last', '11'), (1970, 1825, '_wp_attached_file', '2021/02/9783030597122.jpg'), (1971, 1825, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:153;s:6:"height";i:232;s:4:"file";s:25:"2021/02/9783030597122.jpg";s:5:"sizes";a:1:{s:9:"thumbnail";a:4:{s:4:"file";s:25:"9783030597122-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'), (1972, 1825, '_wp_attachment_image_alt', 'Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II'), (1973, 1824, 'title', 'Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II'), (1974, 1824, '_title', 'field_5ff86849c7493'), (1975, 1824, 'authors', 'Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., Joskowicz, L. (Eds.)'), (1976, 1824, '_authors', 'field_5ff868da50eec'), (1977, 1824, 'date', '20201004'), (1978, 1824, '_date', 'field_5ff868f750eed'), (1979, 1824, 'journal', 'Springer, LNCS 12262'), (1980, 1824, '_journal', 'field_5ff8692750eee'), (1981, 1824, 'keywords', 'a:2:{i:0;s:2:"28";i:1;s:2:"26";}'), (1982, 1824, '_keywords', 'field_5ff8695650eef'), (1983, 1824, 'publisher_link', 'https://www.springer.com/gp/book/9783030597122'), (1984, 1824, '_publisher_link', 'field_5ff8696250ef0'), (1985, 1824, 'open_access_link', ''), (1986, 1824, '_open_access_link', 'field_5ffca31d9e5f6'), (1987, 1824, 'image', '1825'), (1988, 1824, '_image', 'field_5ffcb3a32211f'), (1989, 1824, 'caption', 'MICCAI 2020 - Proceedings (Part II - image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks)'), (1990, 1824, '_caption', 'field_5ffd5f2b99c98'), (1991, 1824, 'abstract', '
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020.
The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process.
'), (1992, 1824, '_abstract', 'field_5ff869ab50ef3'), (1993, 1824, 'description', '
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020.
The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections:
Part I: machine learning methodologies
Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks
Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis
Part IV: segmentation; shape models and landmark detection
Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology
Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging
Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
'), (1994, 1824, '_description', 'field_5ff869cb50ef5'), (1995, 1826, '_edit_lock', '1613550093:11'), (1996, 1827, '_wp_attached_file', '2021/02/9783030597153.jpg'), (1997, 1827, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:153;s:6:"height";i:232;s:4:"file";s:25:"2021/02/9783030597153.jpg";s:5:"sizes";a:1:{s:9:"thumbnail";a:4:{s:4:"file";s:25:"9783030597153-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:0:"";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:0:"";s:11:"orientation";s:1:"0";s:8:"keywords";a:0:{}}}'), (1998, 1827, '_wp_attachment_image_alt', 'Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III'), (1999, 1826, '_edit_last', '11'), (2000, 1826, 'title', 'Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III'), (2001, 1826, '_title', 'field_5ff86849c7493'), (2002, 1826, 'authors', 'Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., Joskowicz, L. (Eds.)'), (2003, 1826, '_authors', 'field_5ff868da50eec'), (2004, 1826, 'date', '20201004'), (2005, 1826, '_date', 'field_5ff868f750eed'), (2006, 1826, 'journal', 'Springer, LNCS 12263'), (2007, 1826, '_journal', 'field_5ff8692750eee'), (2008, 1826, 'keywords', ''), (2009, 1826, '_keywords', 'field_5ff8695650eef'), (2010, 1826, 'publisher_link', 'https://www.springer.com/gp/book/9783030597153'), (2011, 1826, '_publisher_link', 'field_5ff8696250ef0'), (2012, 1826, 'open_access_link', ''), (2013, 1826, '_open_access_link', 'field_5ffca31d9e5f6'), (2014, 1826, 'image', '1827'), (2015, 1826, '_image', 'field_5ffcb3a32211f'), (2016, 1826, 'caption', 'MICCAI 2020 - Proceedings (Part III - CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis)'), (2017, 1826, '_caption', 'field_5ffd5f2b99c98'), (2018, 1826, 'abstract', '
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020.
The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process.
'), (2019, 1826, '_abstract', 'field_5ff869ab50ef3'), (2020, 1826, 'description', '
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020.
The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections:
Part I: machine learning methodologies
Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks
Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis
Part IV: segmentation; shape models and landmark detection
Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology
Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging
Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
'), (2021, 1826, '_description', 'field_5ff869cb50ef5'), (2022, 1828, '_edit_lock', '1616406335:11'), (2023, 1828, '_edit_last', '11'), (2026, 1830, '_wp_attached_file', '2021/02/these_IHI_EN-v1.pdf'), (2027, 1830, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:23:"these_IHI_EN-v1-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:31:"these_IHI_EN-v1-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:32:"these_IHI_EN-v1-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:31:"these_IHI_EN-v1-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'), (2028, 1831, '_wp_attached_file', '2021/02/these_MS_EN-v1.pdf'), (2029, 1831, '_wp_attachment_metadata', 'a:1:{s:5:"sizes";a:4:{s:4:"full";a:4:{s:4:"file";s:22:"these_MS_EN-v1-pdf.jpg";s:5:"width";i:1058;s:6:"height";i:1497;s:9:"mime-type";s:10:"image/jpeg";}s:6:"medium";a:4:{s:4:"file";s:30:"these_MS_EN-v1-pdf-212x300.jpg";s:5:"width";i:212;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:5:"large";a:4:{s:4:"file";s:31:"these_MS_EN-v1-pdf-724x1024.jpg";s:5:"width";i:724;s:6:"height";i:1024;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:30:"these_MS_EN-v1-pdf-106x150.jpg";s:5:"width";i:106;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}}}'), (2030, 1834, '_wp_attached_file', '2018/11/omarelrifai.jpg'), (2031, 1834, '_wp_attachment_metadata', 'a:5:{s:5:"width";i:774;s:6:"height";i:916;s:4:"file";s:23:"2018/11/omarelrifai.jpg";s:5:"sizes";a:3:{s:6:"medium";a:4:{s:4:"file";s:23:"omarelrifai-253x300.jpg";s:5:"width";i:253;s:6:"height";i:300;s:9:"mime-type";s:10:"image/jpeg";}s:9:"thumbnail";a:4:{s:4:"file";s:23:"omarelrifai-150x150.jpg";s:5:"width";i:150;s:6:"height";i:150;s:9:"mime-type";s:10:"image/jpeg";}s:12:"medium_large";a:4:{s:4:"file";s:23:"omarelrifai-768x909.jpg";s:5:"width";i:768;s:6:"height";i:909;s:9:"mime-type";s:10:"image/jpeg";}}s:10:"image_meta";a:12:{s:8:"aperture";s:1:"0";s:6:"credit";s:0:"";s:6:"camera";s:0:"";s:7:"caption";s:11:"KM_C308 Q76";s:17:"created_timestamp";s:1:"0";s:9:"copyright";s:0:"";s:12:"focal_length";s:1:"0";s:3:"iso";s:1:"0";s:13:"shutter_speed";s:1:"0";s:5:"title";s:11:"KM_C308 Q76";s:11:"orientation";s:1:"1";s:8:"keywords";a:0:{}}}'), (2032, 1835, '_edit_lock', '1618908373:8'), (2033, 1835, '_edit_last', '11'), (2038, 1835, 'title', 'BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks'), (2039, 1835, '_title', 'field_5ff86849c7493'), (2040, 1835, 'authors', 'Marie-Constance Corsi, Mario Chavez D.Schwartz, Nathalie George, Laurent Hugueville, Ari E. Kahn, Sophie Dupont, Danielle S. Bassett, Fabrizio De Vico Fallani'), (2041, 1835, '_authors', 'field_5ff868da50eec'), (2042, 1835, 'date', '20210316'), (2043, 1835, '_date', 'field_5ff868f750eed'), (2044, 1835, 'journal', 'Journal of Neural Engineering'), (2045, 1835, '_journal', 'field_5ff8692750eee'), (2046, 1835, 'keywords', 'a:5:{i:0;s:2:"40";i:1;s:2:"41";i:2;s:2:"42";i:3;s:2:"43";i:4;s:2:"44";}'), (2047, 1835, '_keywords', 'field_5ff8695650eef'), (2048, 1835, 'publisher_link', 'https://pubmed.ncbi.nlm.nih.gov/33725682/'), (2049, 1835, '_publisher_link', 'field_5ff8696250ef0'), (2050, 1835, 'open_access_link', 'https://hal.inria.fr/hal-03171591/'), (2051, 1835, '_open_access_link', 'field_5ffca31d9e5f6'), (2052, 1835, 'image', '1839'), (2053, 1835, '_image', 'field_5ffcb3a32211f'), (2054, 1835, 'caption', 'Behavioral performance and E/MEG contributions. (A) Distribution of BCI accuracy scores averaged across the runs of each session. Horizontal lines inside the box represent the median values. (B) Evolution of the E/MEG networks over sessions (average over the participants), obtained for each session, and condition within the alpha2 (top) and beta1 (bottom) ranges. (C) Evolution of attributed weights over sessions within the alpha2 (top) and beta1 (bottom) ranges. We plotted in grey and green the weight distribution associated, respectively, with EEG and MEG. Horizontal lines inside the box represent the median values.'), (2055, 1835, '_caption', 'field_5ffd5f2b99c98'), (2056, 1835, 'abstract', 'Objective Brain-computer interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive closed-loop systems remains a learned skill that is dicult to develop for a non-negligible proportion of users. The involved learning process induces neural changes associated with a brain network reorganization that remains poorly understood. Approach To address this inter-subject variability, we adopted a multilayer approach to integrate brain network properties from electroencephalographic (EEG) and magnetoencephalographic (MEG) data resulting from a four-session BCI training program followed by a group of healthy subjects. Our method gives access to the contribution of each layer to multilayer network that tends to be equal with time. Main results We show that regardless the chosen modality, a progressive increase in the integration of somatosensory areas in the alpha band was paralleled by a decrease of the integration of visual processing and working memory areas in the beta band. Notably, only brain network properties in multilayer network correlated with future BCI scores in the alpha 2 band: positively in somatosensory and decision making related areas and negatively in associative areas. Significance Our findings cast new light on neural processes underlying BCI training. Integrating multimodal brain network properties provides new information that correlates with behavioral performance and could be considered as a potential marker of BCI learning.'), (2057, 1835, '_abstract', 'field_5ff869ab50ef3'), (2058, 1835, 'description', 'Reaching a high performance in controlling a brain-computer interface requires several sessions of training. Even though previous studies suggested the involvement of a distributed network, neural mechanisms underlying this learning process remains poorly understood. A recent work of our group led by J. Guillon proved that combining multimodal neuroimaging data from a network perspective can reveal properties that cannot be detected by approaches relying on a single modality. In this study, we integrated multimodal brain network properties from electroencephalography (EEG) and magnetoencephalography (MEG), known to be complementary. More specifically, we studied coreness properties, defined as the probability for a given node to belong to a group of tightly connected nodes. We computed these properties both at the single modality level (EEG or MEG) and at the integrated level. We observed similar trends in the evolution of the network properties over the BCI training sessions between single and multimodal levels. Notably, we obtained a significant association with the future BCI performance only in the case of the coreness properties resulting from the M/EEG integration. 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Bottom to top rows show alteration of brain glucose metabolism, hippocampus atrophy, cortical thinning and onset of cognitive decline. Black arrows and ellipses indicate some areas of great changes'), (2155, 1852, '_caption', 'field_5ffd5f2b99c98'), (2156, 1852, 'abstract', 'Alzheimer’s disease (AD) is characterized by the progressive alterations seen in brain images which give rise to the onset of various sets of symptoms. The variability in the dynamics of changes in both brain images and cognitive impairments remains poorly understood. This paper introduces AD Course Map a spatiotemporal atlas of Alzheimer’s disease progression. It summarizes the variability in the progression of a series of neuropsychological assessments, the propagation of hypometabolism and cortical thinning across brain regions and the deformation of the shape of the hippocampus. The analysis of these variations highlights strong genetic determinants for the progression, like possible compensatory mechanisms at play during disease progression. AD Course Map also predicts the patient’s cognitive decline with a better accuracy than the 56 methods benchmarked in the open challenge TADPOLE. Finally, AD Course Map is used to simulate cohorts of virtual patients developing Alzheimer’s disease. AD Course Map offers therefore new tools for exploring the progression of AD and personalizing patients care.'), (2157, 1852, '_abstract', 'field_5ff869ab50ef3'), (2158, 1852, 'description', '

In this paper, we have been able to simultaneously characterize the progression of cognitive assessments, the cortical thickness, meshes of the hippocampus and glucose consumption measured with PET-FDG over a period of 30 years during the course of Alzheimer’s disease.

Such a description allows to precisely quantify the influence of different cofactors on the disease progression. But more importantly, this description of unprecedented precision allows to position any individual on the disease timeline in order to forecast the values of his or her modalities up to 4 years ahead.

Some results are presented on www.digital-brain.org

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Faculty

[tmm name="faculty"]

Post-docs

[tmm name="post-docs"]

PhD Students

[tmm name="phd-students"]

Engineers

[tmm name="engineers"]

Associate fellows

[tmm name="collaborators"]

Administrative staff

[tmm name="assistant"]

Former Members

Left in 2024: Left in 2023: Left in 2022: Left in 2021: Left in 2020:   Left in 2019 :   Left in 2018 and before: ', 'Team Members', '', 'publish', 'open', 'open', '', 'team-members', '', '', '2024-03-21 15:41:14', '2024-03-21 14:41:14', '', 0, 'https://www.aramislab.fr/?page_id=4', 2, 'page', '', (5, 1, '2014-02-06 09:05:16', '2014-02-06 09:05:16', '', 'Team Members', '', 'inherit', 'open', 'open', '', '4-revision-v1', '', '', '2014-02-06 09:05:16', '2014-02-06 09:05:16', '', 4, 'https://www.aramislab.fr/?p=5', 0, 'revision', '', (22, 1, '2014-02-06 10:57:24', '2014-02-06 09:57:24', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed capturing various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, multiple sclerosis, Parkinson\'s disease...). They shall allow deepening our understanding of neurological diseases and developing new decision support systems for diagnosis, prognosis and design of clinical trials.
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New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'publish', 'open', 'open', '', 'research-topics', '', '', '2020-09-09 18:06:35', '2020-09-09 17:06:35', '', 0, 'https://www.aramislab.fr/?page_id=22', 3, 'page', '', (25, 1, '2014-02-06 10:57:24', '2014-02-06 09:57:24', '', 'Research topics', '', 'inherit', 'open', 'open', '', '22-revision-v1', '', '', '2014-02-06 10:57:24', '2014-02-06 09:57:24', '', 22, 'https://www.aramislab.fr/?p=25', 0, 'revision', '', (26, 1, '2014-02-06 11:00:26', '2014-02-06 10:00:26', '

Most representative publications

Networks

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

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Machine Learning

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinical studies

 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publication overview', '', 'publish', 'open', 'closed', '', 'publication-overview', '', '', '2021-01-08 14:29:58', '2021-01-08 13:29:58', '', 0, 'https://www.aramislab.fr/?page_id=26', 4, 'page', '', (29, 1, '2014-02-06 11:00:26', '2014-02-06 10:00:26', '', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-02-06 11:00:26', '2014-02-06 10:00:26', '', 26, 'https://www.aramislab.fr/?p=29', 0, 'revision', '', (30, 1, '2014-02-06 11:00:36', '2014-02-06 10:00:36', '
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and varied expertise (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).

Postdocs / Scientists

PhD thesis

Engineers / Software developers

  • Research Engineer - Deep learning for brain image analysis - Starting date:  as soon as possible - Contact: camille.brianceau@icm- institute.org

 Master 2 Internships / Stages de Master 2

', 'Job offers', '', 'publish', 'open', 'closed', '', 'job-offers', '', '', '2024-06-28 12:15:49', '2024-06-28 11:15:49', '', 0, 'https://www.aramislab.fr/?page_id=30', 5, 'page', '', (33, 1, '2014-02-06 11:00:36', '2014-02-06 10:00:36', '', 'Job offers', '', 'inherit', 'open', 'open', '', '30-revision-v1', '', '', '2014-02-06 11:00:36', '2014-02-06 10:00:36', '', 30, 'https://www.aramislab.fr/?p=33', 0, 'revision', '', (41, 1, '2014-02-11 18:16:35', '2014-02-11 17:16:35', '

Head

  • Olivier Colliot - CNRS Researcher (CR1, HDR)

Deputy Head

  • Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP

Researchers

  • Mario Chavez - CNRS Researcher (CR1)
  • Marie Chupin - CNRS Research Engineer (IR)
  • Stanley Durrleman - INRIA Researcher

Medical Faculty / Clinicians

  • Claude Adam  - Neurologist (PH), AP-HP
  • Anne Bertrand - Neuroradiologist (CCA), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie
  • Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP

Associated engineers of the neuroimaging core facility

  • Eric Bardinet - CNRS Research Engineer
  • Sara Fernandez-Vidal - Inserm Research Engineer
  • Laurent Hugueville - CNRS Engineer
  • Denis Schwartz - Inserm Research Engineer
  • Romain Valabrègue - Inserm Research Engineer

Administrative assistants

  • Cindy Crossouard - INRIA
  • Thomas Estienne - UPMC, CATI project
  • Corinne Omer - CNRS

Engineers

  • Hugo Dary - UPMC, CATI project
  • Ludovic Fillon - UPMC, CATI project
  • Alexandre Routier - UPMC, CATI project
  • François Touvet - INRIA

Clinical Research Associates

  • Chabha Azouani - ICM, CATI project
  • Xavier Badé - UPMC, CATI project
  • Ali Bouyahia - ICM, CATI project
  • Johanne Germain - UPMC, CATI project

PhD students

  • Claire Cury - CNRS/UPMC
  • Barbara Gris - ENS de Cachan
  • Pietro Gori - INRIA/UPMC
  • Takoua Kaaouana - UPMC, CATI project
  • Jean-Baptiste Schiratti - Ecole Polytechnique

', 'Team Members', '', 'inherit', 'open', 'open', '', '4-revision-v1', '', '', '2014-02-11 18:16:35', '2014-02-11 17:16:35', '', 4, 'https://www.aramislab.fr/?p=41', 0, 'revision', '', (42, 1, '2014-02-11 18:19:45', '2014-02-11 17:19:45', '

Direction

  • Olivier Colliot - CNRS Researcher (CR1, HDR) - Team Leader
  • Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - Co-Team Leader

Deputy Head

  • Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP

Researchers

  • Mario Chavez - CNRS Researcher (CR1)
  • Marie Chupin - CNRS Research Engineer (IR)
  • Stanley Durrleman - INRIA Researcher

Medical Faculty / Clinicians

  • Claude Adam  - Neurologist (PH), AP-HP
  • Anne Bertrand - Neuroradiologist (CCA), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie
  • Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP

Associated engineers of the neuroimaging core facility

  • Eric Bardinet - CNRS Research Engineer
  • Sara Fernandez-Vidal - Inserm Research Engineer
  • Laurent Hugueville - CNRS Engineer
  • Denis Schwartz - Inserm Research Engineer
  • Romain Valabrègue - Inserm Research Engineer

Administrative assistants

  • Cindy Crossouard - INRIA
  • Thomas Estienne - UPMC, CATI project
  • Corinne Omer - CNRS

Engineers

  • Yohan Attal - UPMC
  • Fabrizio de Vico Fallani - IHU-A-ICM
  • Ana Fouquier - CNRS
  • Linda Marrakchi-Kacem - UPMC, CATI project

Engineers

  • Hugo Dary - UPMC, CATI project
  • Ludovic Fillon - UPMC, CATI project
  • Alexandre Routier - UPMC, CATI project
  • François Touvet - INRIA

Clinical Research Associates

  • Chabha Azouani - ICM, CATI project
  • Xavier Badé - UPMC, CATI project
  • Ali Bouyahia - ICM, CATI project
  • Johanne Germain - UPMC, CATI project

PhD students

  • Claire Cury - CNRS/UPMC
  • Barbara Gris - ENS de Cachan
  • Pietro Gori - INRIA/UPMC
  • Takoua Kaaouana - UPMC, CATI project
  • Jean-Baptiste Schiratti - Ecole Polytechnique

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Direction

  • Olivier Colliot - CNRS Researcher (CR1, HDR) - Team Leader
  • Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - Co-Team Leader

Deputy Head

  • Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP

Researchers

  • Mario Chavez - CNRS Researcher (CR1)
  • Marie Chupin - CNRS Research Engineer (IR)
  • Stanley Durrleman - INRIA Researcher

Medical Faculty / Clinicians

  • Claude Adam  - Neurologist (PH), AP-HP
  • Anne Bertrand - Neuroradiologist (CCA), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie
  • Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP

Associated engineers of the neuroimaging core facility

  • Eric Bardinet - CNRS Research Engineer
  • Sara Fernandez-Vidal - Inserm Research Engineer
  • Laurent Hugueville - CNRS Engineer
  • Denis Schwartz - Inserm Research Engineer
  • Romain Valabrègue - Inserm Research Engineer

Administrative assistants

  • Cindy Crossouard - INRIA
  • Thomas Estienne - UPMC, CATI project
  • Corinne Omer - CNRS

Postdocs

  • Yohan Attal - UPMC
  • Fabrizio de Vico Fallani - IHU-A-ICM
  • Ana Fouquier - CNRS
  • Linda Marrakchi-Kacem - UPMC, CATI project

Engineers

  • Hugo Dary - UPMC, CATI project
  • Ludovic Fillon - UPMC, CATI project
  • Alexandre Routier - UPMC, CATI project
  • François Touvet - INRIA

Clinical Research Associates

  • Chabha Azouani - ICM, CATI project
  • Xavier Badé - UPMC, CATI project
  • Ali Bouyahia - ICM, CATI project
  • Johanne Germain - UPMC, CATI project

PhD students

  • Claire Cury - CNRS/UPMC
  • Barbara Gris - ENS de Cachan
  • Pietro Gori - INRIA/UPMC
  • Takoua Kaaouana - UPMC, CATI project
  • Jean-Baptiste Schiratti - Ecole Polytechnique

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Direction

  • Olivier Colliot - CNRS Researcher (CR1, HDR) - Team Leader
  • Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - Co-Team Leader

Deputy Head

  • Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP

Researchers

  • Mario Chavez - CNRS Researcher (CR1)
  • Marie Chupin - CNRS Research Engineer (IR)
  • Stanley Durrleman - INRIA Researcher

Medical Faculty / Clinicians

  • Claude Adam  - Neurologist (PH), AP-HP
  • Anne Bertrand - Neuroradiologist (CCA), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie
  • Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP

Associated engineers of the neuroimaging core facility

  • Eric Bardinet - CNRS Research Engineer
  • Sara Fernandez-Vidal - Inserm Research Engineer
  • Laurent Hugueville - CNRS Engineer
  • Denis Schwartz - Inserm Research Engineer
  • Romain Valabrègue - Inserm Research Engineer

Administrative assistants

  • Cindy Crossouard - INRIA
  • Thomas Estienne - UPMC, CATI project
  • Corinne Omer - CNRS

Postdocs

  • Yohan Attal - UPMC
  • Fabrizio de Vico Fallani - IHU-A-ICM
  • Ana Fouquier - CNRS
  • Linda Marrakchi-Kacem - UPMC, CATI project

Engineers

  • Hugo Dary - UPMC, CATI project
  • Ludovic Fillon - UPMC, CATI project
  • Alexandre Routier - UPMC, CATI project
  • François Touvet - INRIA

Clinical Research Associates

  • Chabha Azouani - ICM, CATI project
  • Xavier Badé - UPMC, CATI project
  • Ali Bouyahia - ICM, CATI project
  • Johanne Germain - UPMC, CATI project

PhD students

  • Claire Cury - CNRS/UPMC
  • Barbara Gris - ENS de Cachan
  • Pietro Gori - INRIA/UPMC
  • Takoua Kaaouana - UPMC, CATI project
  • Jean-Baptiste Schiratti - École Polytechnique

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Head

  • Olivier Colliot - CNRS Researcher (CR1, HDR)

Deputy Head

  • Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP

Researchers

  • Mario Chavez - CNRS Researcher (CR1)
  • Marie Chupin - CNRS Research Engineer (IR)
  • Stanley Durrleman - INRIA Researcher

Medical Faculty / Clinicians

  • Claude Adam  - Neurologist (PH), AP-HP
  • Anne Bertrand - Neuroradiologist (CCA), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie
  • Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP

Associated engineers of the neuroimaging core facility

  • Eric Bardinet - CNRS Research Engineer
  • Sara Fernandez-Vidal - Inserm Research Engineer
  • Laurent Hugueville - CNRS Engineer
  • Denis Schwartz - Inserm Research Engineer
  • Romain Valabrègue - Inserm Research Engineer

Administrative assistants

  • Cindy Crossouard - INRIA
  • Thomas Estienne - UPMC, CATI project
  • Corinne Omer - CNRS

Postdocs

  • Yohan Attal - UPMC
  • Fabrizio de Vico Fallani - IHU-A-ICM
  • Ana Fouquier - CNRS
  • Linda Marrakchi-Kacem - UPMC, CATI project

Engineers

  • Hugo Dary - UPMC, CATI project
  • Ludovic Fillon - UPMC, CATI project
  • Alexandre Routier - UPMC, CATI project
  • François Touvet - INRIA

Clinical Research Associates

  • Chabha Azouani - ICM, CATI project
  • Xavier Badé - UPMC, CATI project
  • Ali Bouyahia - ICM, CATI project
  • Johanne Germain - UPMC, CATI project

PhD students

  • Claire Cury - CNRS/UPMC
  • Barbara Gris - ENS de Cachan
  • Pietro Gori - INRIA/UPMC
  • Takoua Kaaouana - UPMC, CATI project
  • Jean-Baptiste Schiratti - École Polytechnique

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If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and varied expertise (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).

Postdocs / Scientists

PhD thesis

Engineers / Software developers

  • Research Engineer - Deep learning for brain image analysis - Starting date:  as soon as possible - Contact: camille.brianceau@icm- institute.org

 Master 2 Internships / Stages de Master 2

', 'Job offers', '', 'inherit', 'open', 'open', '', '30-autosave-v1', '', '', '2024-06-28 12:15:27', '2024-06-28 11:15:27', '', 30, 'https://www.aramislab.fr/?p=56', 0, 'revision', '', (57, 1, '2014-02-12 19:01:32', '2014-02-12 18:01:32', '

Available positions

Postdocs

Shape analysis of brain structures using 7T MRI  

Master Internships / Stages de Master

   ', 'Job offers', '', 'inherit', 'open', 'open', '', '30-revision-v1', '', '', '2014-02-12 19:01:32', '2014-02-12 18:01:32', '', 30, 'https://www.aramislab.fr/?p=57', 0, 'revision', '', (58, 1, '2014-02-13 16:12:20', '2014-02-13 15:12:20', '

Postdocs

Shape analysis of brain structures using 7T MRI  

Master Internships / Stages de Master

 

Other positions

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Head

Deputy Head

Researchers

Medical Faculty / Clinicians

Associated engineers of the neuroimaging core facility

Administrative assistants

Postdocs

Engineers

Clinical Research Associates

PhD students

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[HAL] URL haltool [/HAL]
', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-02-28 18:39:50', '2014-02-28 17:39:50', '', 26, 'https://www.aramislab.fr/?p=66', 0, 'revision', '', (67, 1, '2014-02-28 18:40:18', '2014-02-28 17:40:18', '[HAL] URL haltool [/HAL]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-02-28 18:40:18', '2014-02-28 17:40:18', '', 26, 'https://www.aramislab.fr/?p=67', 0, 'revision', '', (68, 1, '2014-02-28 18:49:26', '2014-02-28 17:49:26', '[HAL] https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_auteur=oui&CB_titre=oui&CB_article=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css [/HAL]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-02-28 18:49:26', '2014-02-28 17:49:26', '', 26, 'https://www.aramislab.fr/?p=68', 0, 'revision', '', (69, 1, '2014-02-28 18:50:46', '2014-02-28 17:50:46', '[HAL] http://hal.inria.fr/lab/aramis/ [/HAL]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-02-28 18:50:46', '2014-02-28 17:50:46', '', 26, 'https://www.aramislab.fr/?p=69', 0, 'revision', '', (72, 1, '2014-02-28 18:52:33', '2014-02-28 17:52:33', '[HAL] http://hal.inria.fr/lab/aramis/ [/HAL]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-02-28 18:52:33', '2014-02-28 17:52:33', '', 26, 'https://www.aramislab.fr/?p=72', 0, 'revision', '', (73, 1, '2018-11-21 18:12:59', '2018-11-21 17:12:59', '

Networks

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]  

Longitudinal

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinical studies

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]  

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'open', 'open', '', '26-autosave-v1', '', '', '2018-11-21 18:12:59', '2018-11-21 17:12:59', '', 26, 'https://www.aramislab.fr/?p=73', 0, 'revision', '', (74, 1, '2014-02-28 19:02:48', '2014-02-28 18:02:48', '[HAL] http://hal.inria.fr/lab/aramis/ [/HAL] [BASTRIEN] ARAMIS [/BASTRIEN]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-02-28 19:02:48', '2014-02-28 18:02:48', '', 26, 'https://www.aramislab.fr/?p=74', 0, 'revision', '', (75, 1, '2014-02-28 19:05:02', '2014-02-28 18:05:02', '[HAL] http://hal.inria.fr/lab/aramis/ [/HAL] [BASTRIEN] aramis [/BASTRIEN]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-02-28 19:05:02', '2014-02-28 18:05:02', '', 26, 'https://www.aramislab.fr/?p=75', 0, 'revision', '', (76, 1, '2014-02-28 19:05:25', '2014-02-28 18:05:25', '[HALEN] http://hal.inria.fr/lab/aramis/ [/HALEN] [BASTRIFR] aramis [/BASTRIFR]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-02-28 19:05:25', '2014-02-28 18:05:25', '', 26, 'https://www.aramislab.fr/?p=76', 0, 'revision', '', (77, 1, '2014-02-28 19:05:56', '2014-02-28 18:05:56', '[HAL] http://hal.inria.fr/lab/aramis/ [/HAL] [BASTRIEN] NOM aramis [/BASTRIEN]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-02-28 19:05:56', '2014-02-28 18:05:56', '', 26, 'https://www.aramislab.fr/?p=77', 0, 'revision', '', (78, 1, '2014-02-28 19:13:31', '2014-02-28 18:13:31', '[HAL] https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_ref_biblio=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css [/HAL] [BASTRIEN] aramis [/BASTRIEN]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-02-28 19:13:31', '2014-02-28 18:13:31', '', 26, 'https://www.aramislab.fr/?p=78', 0, 'revision', '', (80, 1, '2014-03-03 10:30:51', '2014-03-03 09:30:51', '10 most representative publications   Publications from HAL [HAL] https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_ref_biblio=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css [/HAL] [BASTRIEN] aramis [/BASTRIEN]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-03-03 10:30:51', '2014-03-03 09:30:51', '', 26, 'https://www.aramislab.fr/?p=80', 0, 'revision', '', (81, 1, '2014-03-03 10:32:18', '2014-03-03 09:32:18', '

Ten most representative publications

 

Publications from HAL

[HAL] https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_ref_biblio=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css [/HAL] [BASTRIEN] aramis [/BASTRIEN]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-03-03 10:32:18', '2014-03-03 09:32:18', '', 26, 'https://www.aramislab.fr/?p=81', 0, 'revision', '', (82, 1, '2014-03-03 11:29:10', '2014-03-03 10:29:10', '

Ten most representative publications

 

Publications from HAL

[HAL]https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_ref_biblio=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css[/HAL] [BASTRIEN] aramis [/BASTRIEN]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-03-03 11:29:10', '2014-03-03 10:29:10', '', 26, 'https://www.aramislab.fr/?p=82', 0, 'revision', '', (83, 1, '2014-03-03 11:31:40', '2014-03-03 10:31:40', '

Ten most representative publications

 

Publications from HAL

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Ten most representative publications

 

Publications from HAL

[HAL]https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_ref_biblio=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css[/HAL] [BASTRIEN] aramis [/BASTRIEN]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-03-03 11:32:14', '2014-03-03 10:32:14', '', 26, 'https://www.aramislab.fr/?p=84', 0, 'revision', '', (85, 1, '2014-03-03 11:32:29', '2014-03-03 10:32:29', '

Ten most representative publications

 

Publications from HAL

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Ten most representative publications

haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_ref_biblio=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css

Publications from HAL

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Ten most representative publications

haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_ref_biblio=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css

Publications from HAL

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Ten most representative publications

haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_ref_biblio=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css

Publications from HAL

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Ten most representative publications

haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_ref_biblio=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css

Publications from HAL

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Ten most representative publications

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Publications from HAL

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Ten most representative publications

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Ten most representative publications

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Publications from HAL

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Ten most representative publications

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Publications from HAL

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Ten most representative publications

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Publications from HAL

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Ten most representative publications

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Publications from HAL

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Ten most representative publications

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Publications from HAL

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Ten most representative publications

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Publications from HAL

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Most representative publications

Full list of publications

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Ten most representative publications

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Publications from HAL

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[su_list icon_color="#010107"]webpage[/su_list]Olivier Colliot - CNRS Researcher (CR1, HDR) - email: olivier.colliot [at] upmc.fr

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Head

Olivier Colliot - CNRS Researcher (CR1, HDR) - email: olivier.colliot [at] upmc.fr

Deputy Head

Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

Mario Chavez- CNRS Researcher (CR1) - email:

Marie Chupin - CNRS Research Engineer (IR) - email:

Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

Associated engineers of the neuroimaging core facility

Administrative assistants

Postdocs

Engineers

Clinical Research Associates

PhD students

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Head

- Olivier Colliot - CNRS Researcher (CR1, HDR) - email: olivier.colliot [at] upmc.fr

Deputy Head

- Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

Mario Chavez- CNRS Researcher (CR1) - email:

Marie Chupin - CNRS Research Engineer (IR) - email:

Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

Associated engineers of the neuroimaging core facility

Administrative assistants

Postdocs

Engineers

Clinical Research Associates

PhD students

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Head

      - Olivier Colliot - CNRS Researcher (CR1, HDR) - email: olivier.colliot [at] upmc.fr

Deputy Head

- Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

- Mario Chavez- CNRS Researcher (CR1) - email:
- Marie Chupin - CNRS Research Engineer (IR) - email:
     -    Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

Associated engineers of the neuroimaging core facility

Administrative assistants

Postdocs

Engineers

Clinical Research Associates

PhD students

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Head

      - Olivier Colliot - CNRS Researcher (CR1, HDR) - email: olivier.colliot [at] upmc.fr

Deputy Head

- Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:
     -   Marie Chupin - CNRS Research Engineer (IR) - email:
     -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

Associated engineers of the neuroimaging core facility

Administrative assistants

Postdocs

Engineers

Clinical Research Associates

PhD students

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Head

     -   Olivier Colliot - CNRS Researcher (CR1, HDR) - email: olivier.colliot [at] upmc.fr

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:
     -   Marie Chupin - CNRS Research Engineer (IR) - email:
     -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

Associated engineers of the neuroimaging core facility

Administrative assistants

Postdocs

Engineers

Clinical Research Associates

PhD students

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Head

     -   Olivier Colliot - CNRS Researcher (CR1, HDR) - email: olivier.colliot [at] upmc.fr

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:
     -   Marie Chupin - CNRS Research Engineer (IR) - email:
     -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

Associated engineers of the neuroimaging core facility

Administrative assistants

Postdocs

Engineers

Clinical Research Associates

PhD students

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Head

     -   Olivier Colliot - CNRS Researcher (CR1, HDR) - email: olivier.colliot [at] upmc.fr

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:
     -   Marie Chupin - CNRS Research Engineer (IR) - email:
     -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

Associated engineers of the neuroimaging core facility

Administrative assistants

Postdocs

Engineers

Clinical Research Associates

PhD students

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Head

     -   Olivier Colliot - CNRS Researcher (CR1, HDR) - email: olivier.colliot [at] upmc.fr

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:
     -   Marie Chupin - CNRS Research Engineer (IR) - email:
     -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Administrative assistants

Postdocs

Engineers

Clinical Research Associates

PhD students

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Head

     -   Olivier Colliot - CNRS Researcher (CR1, HDR) - email: olivier.colliot [at] upmc.fr

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:
     -   Marie Chupin - CNRS Research Engineer (IR) - email:
     -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Administrative assistants

Postdocs

Engineers

Clinical Research Associates

PhD students

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Head

     -   Olivier Colliot - CNRS Researcher (CR1, HDR) - email: olivier.colliot [at] upmc.fr

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:
     -   Marie Chupin - CNRS Research Engineer (IR) - email:
     -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Administrative assistants

Postdocs

Engineers

Clinical Research Associates

PhD students

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Head

     -   Olivier Colliot - CNRS Researcher (CR1, HDR) - email: olivier.colliot [at] upmc.fr

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:
     -   Marie Chupin - CNRS Research Engineer (IR) - email:
     -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Eric Bardinet - CNRS Research Engineer - email:
Sara Fernandez-Vidal - Inserm Research Engineer - email:
Laurent Hugueville - CNRS Engineer - email:
Denis Schwartz - Inserm Research Engineer - email:
Romain Valabrègue - Inserm Research Engineer - email:

Administrative assistants

Cindy Crossouard - INRIA - email
Thomas Estienne - UPMC, CATI project - email:
Corinne Omer - CNRS - email:

Postdocs

Yohan Attal - UPMC - email:
Fabrizio de Vico Fallani - IHU-A-ICM - email:
Ana Fouquier - CNRS - email:
Linda Marrakchi-Kacem - UPMC, CATI project - email:

Engineers

Hugo Dary - UPMC, CATI project - email:
Ludovic Fillon - UPMC, CATI project - email:
Alexandre Routier - UPMC, CATI project - email:
François Touvet - INRIA - email:

Clinical Research Associates

Chabha Azouani - ICM, CATI project - email:
Xavier Badé - UPMC, CATI project - email:
Ali Bouyahia - ICM, CATI project - email:
Johanne Germain - UPMC, CATI project - email:

PhD students

Claire Cury - CNRS/UPMC - email:
Barbara Gris - ENS de Cachan - email:
Pietro Gori - INRIA/UPMC - email: pietro.gori [at] inria.fr
Takoua Kaaouana - UPMC, CATI project - email:
Jean-Baptiste Schiratti - École Polytechnique - email:

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Head

     -   Olivier Colliot - CNRS Researcher (CR1, HDR) - email: olivier.colliot [at] upmc.fr

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:
     -   Marie Chupin - CNRS Research Engineer (IR) - email:
     -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Eric Bardinet - CNRS Research Engineer - email:
Sara Fernandez-Vidal - Inserm Research Engineer - email:
Laurent Hugueville - CNRS Engineer - email:
Denis Schwartz - Inserm Research Engineer - email:
Romain Valabrègue - Inserm Research Engineer - email:

Administrative assistants

Cindy Crossouard - INRIA - email
Thomas Estienne - UPMC, CATI project - email:
Corinne Omer - CNRS - email:

Postdocs

Yohan Attal - UPMC - email:
Fabrizio de Vico Fallani - IHU-A-ICM - email:
Ana Fouquier - CNRS - email:
Linda Marrakchi-Kacem - UPMC, CATI project - email:

Engineers

Hugo Dary - UPMC, CATI project - email:
Ludovic Fillon - UPMC, CATI project - email:
Alexandre Routier - UPMC, CATI project - email:
François Touvet - INRIA - email:

Clinical Research Associates

Chabha Azouani - ICM, CATI project - email:
Xavier Badé - UPMC, CATI project - email:
Ali Bouyahia - ICM, CATI project - email:
Johanne Germain - UPMC, CATI project - email:

PhD students

Claire Cury - CNRS/UPMC - email:
Barbara Gris - ENS de Cachan - email:
Pietro Gori - INRIA/UPMC - email: pietro.gori [at] inria.fr
Takoua Kaaouana - UPMC, CATI project - email:
Jean-Baptiste Schiratti - École Polytechnique - email:

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Head

     -   Olivier Colliot - CNRS Researcher (CR1, HDR)

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:
     -   Marie Chupin - CNRS Research Engineer (IR) - email:
     -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Eric Bardinet - CNRS Research Engineer - email:
Sara Fernandez-Vidal - Inserm Research Engineer - email:
Laurent Hugueville - CNRS Engineer - email:
Denis Schwartz - Inserm Research Engineer - email:
Romain Valabrègue - Inserm Research Engineer - email:

Administrative assistants

Cindy Crossouard - INRIA - email
Thomas Estienne - UPMC, CATI project - email:
Corinne Omer - CNRS - email:

Postdocs

Yohan Attal - UPMC - email:
Fabrizio de Vico Fallani - IHU-A-ICM - email:
Ana Fouquier - CNRS - email:
Linda Marrakchi-Kacem - UPMC, CATI project - email:

Engineers

Hugo Dary - UPMC, CATI project - email:
Ludovic Fillon - UPMC, CATI project - email:
Alexandre Routier - UPMC, CATI project - email:
François Touvet - INRIA - email:

Clinical Research Associates

Chabha Azouani - ICM, CATI project - email:
Xavier Badé - UPMC, CATI project - email:
Ali Bouyahia - ICM, CATI project - email:
Johanne Germain - UPMC, CATI project - email:

PhD students

Claire Cury - CNRS/UPMC - email:
Barbara Gris - ENS de Cachan - email:
Pietro Gori - INRIA/UPMC - email: pietro.gori [at] inria.fr
Takoua Kaaouana - UPMC, CATI project - email:
Jean-Baptiste Schiratti - École Polytechnique - email:

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Head

     -   Olivier Colliot - CNRS Researcher (CR1, HDR)

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:
     -   Marie Chupin - CNRS Research Engineer (IR) - email:
     -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Eric Bardinet - CNRS Research Engineer - email:
Sara Fernandez-Vidal - Inserm Research Engineer - email:
Laurent Hugueville - CNRS Engineer - email:
Denis Schwartz - Inserm Research Engineer - email:
Romain Valabrègue - Inserm Research Engineer - email:

Administrative assistants

Cindy Crossouard - INRIA - email
Thomas Estienne - UPMC, CATI project - email:
Corinne Omer - CNRS - email:

Postdocs

Yohan Attal - UPMC - email:
Fabrizio de Vico Fallani - IHU-A-ICM - email:
Ana Fouquier - CNRS - email:
Linda Marrakchi-Kacem - UPMC, CATI project - email:

Engineers

Hugo Dary - UPMC, CATI project - email:
Ludovic Fillon - UPMC, CATI project - email:
Alexandre Routier - UPMC, CATI project - email:
François Touvet - INRIA - email:

Clinical Research Associates

Chabha Azouani - ICM, CATI project - email:
Xavier Badé - UPMC, CATI project - email:
Ali Bouyahia - ICM, CATI project - email:
Johanne Germain - UPMC, CATI project - email:

PhD students

Claire Cury - CNRS/UPMC - email:
Barbara Gris - ENS de Cachan - email:
Pietro Gori - INRIA/UPMC - email: pietro.gori [at] inria.fr
Takoua Kaaouana - UPMC, CATI project - email:
Jean-Baptiste Schiratti - École Polytechnique - email:

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Head

     -  Olivier Colliot - CNRS Researcher (CR1, HDR)

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:      -   Marie Chupin - CNRS Research Engineer (IR) - email:      -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Eric Bardinet - CNRS Research Engineer - email:
Sara Fernandez-Vidal - Inserm Research Engineer - email:
Laurent Hugueville - CNRS Engineer - email:
Denis Schwartz - Inserm Research Engineer - email:
Romain Valabrègue - Inserm Research Engineer - email:

Administrative assistants

Cindy Crossouard - INRIA - email
Thomas Estienne - UPMC, CATI project - email:
Corinne Omer - CNRS - email:

Postdocs

Yohan Attal - UPMC - email:
Fabrizio de Vico Fallani - IHU-A-ICM - email:
Ana Fouquier - CNRS - email:
Linda Marrakchi-Kacem - UPMC, CATI project - email:

Engineers

Hugo Dary - UPMC, CATI project - email:
Ludovic Fillon - UPMC, CATI project - email:
Alexandre Routier - UPMC, CATI project - email:
François Touvet - INRIA - email:

Clinical Research Associates

Chabha Azouani - ICM, CATI project - email:
Xavier Badé - UPMC, CATI project - email:
Ali Bouyahia - ICM, CATI project - email:
Johanne Germain - UPMC, CATI project - email:

PhD students

Claire Cury - CNRS/UPMC - email:
Barbara Gris - ENS de Cachan - email:
Pietro Gori - INRIA/UPMC - email: pietro.gori [at] inria.fr
Takoua Kaaouana - UPMC, CATI project - email:
Jean-Baptiste Schiratti - École Polytechnique - email:

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Head

     -  Olivier Colliot - CNRS Researcher (CR1, HDR)

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:      -   Marie Chupin - CNRS Research Engineer (IR) - email:      -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Eric Bardinet - CNRS Research Engineer - email:
Sara Fernandez-Vidal - Inserm Research Engineer - email:
Laurent Hugueville - CNRS Engineer - email:
Denis Schwartz - Inserm Research Engineer - email:
Romain Valabrègue - Inserm Research Engineer - email:

Administrative assistants

Cindy Crossouard - INRIA - email
Thomas Estienne - UPMC, CATI project - email:
Corinne Omer - CNRS - email:

Postdocs

Yohan Attal - UPMC - email:
Fabrizio de Vico Fallani - IHU-A-ICM - email:
Ana Fouquier - CNRS - email:
Linda Marrakchi-Kacem - UPMC, CATI project - email:

Engineers

Hugo Dary - UPMC, CATI project - email:
Ludovic Fillon - UPMC, CATI project - email:
Alexandre Routier - UPMC, CATI project - email:
François Touvet - INRIA - email:

Clinical Research Associates

Chabha Azouani - ICM, CATI project - email:
Xavier Badé - UPMC, CATI project - email:
Ali Bouyahia - ICM, CATI project - email:
Johanne Germain - UPMC, CATI project - email:

PhD students

Claire Cury - CNRS/UPMC - email:
Barbara Gris - ENS de Cachan - email:
Pietro Gori - INRIA/UPMC - email: pietro.gori [at] inria.fr
Takoua Kaaouana - UPMC, CATI project - email:
Jean-Baptiste Schiratti - École Polytechnique - email:

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Head

     -  Olivier Colliot - CNRS Researcher (CR1, HDR)

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:      -   Marie Chupin - CNRS Research Engineer (IR) - email:      -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Eric Bardinet - CNRS Research Engineer - email:
Sara Fernandez-Vidal - Inserm Research Engineer - email:
Laurent Hugueville - CNRS Engineer - email:
Denis Schwartz - Inserm Research Engineer - email:
Romain Valabrègue - Inserm Research Engineer - email:

Administrative assistants

Cindy Crossouard - INRIA - email
Thomas Estienne - UPMC, CATI project - email:
Corinne Omer - CNRS - email:

Postdocs

Yohan Attal - UPMC - email:
Fabrizio de Vico Fallani - IHU-A-ICM - email:
Ana Fouquier - CNRS - email:
Linda Marrakchi-Kacem - UPMC, CATI project - email:

Engineers

Hugo Dary - UPMC, CATI project - email:
Ludovic Fillon - UPMC, CATI project - email:
Alexandre Routier - UPMC, CATI project - email:
François Touvet - INRIA - email:

Clinical Research Associates

Chabha Azouani - ICM, CATI project - email:
Xavier Badé - UPMC, CATI project - email:
Ali Bouyahia - ICM, CATI project - email:
Johanne Germain - UPMC, CATI project - email:

PhD students

Claire Cury - CNRS/UPMC - email:
Barbara Gris - ENS de Cachan - email:
Pietro Gori - INRIA/UPMC - email: pietro.gori [at] inria.fr
Takoua Kaaouana - UPMC, CATI project - email:
Jean-Baptiste Schiratti - École Polytechnique - email:

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Head

Email: olivier.colliot [at] upmc.fr

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:      -   Marie Chupin - CNRS Research Engineer (IR) - email:      -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Eric Bardinet - CNRS Research Engineer - email:
Sara Fernandez-Vidal - Inserm Research Engineer - email:
Laurent Hugueville - CNRS Engineer - email:
Denis Schwartz - Inserm Research Engineer - email:
Romain Valabrègue - Inserm Research Engineer - email:

Administrative assistants

Cindy Crossouard - INRIA - email
Thomas Estienne - UPMC, CATI project - email:
Corinne Omer - CNRS - email:

Postdocs

Yohan Attal - UPMC - email:
Fabrizio de Vico Fallani - IHU-A-ICM - email:
Ana Fouquier - CNRS - email:
Linda Marrakchi-Kacem - UPMC, CATI project - email:

Engineers

Hugo Dary - UPMC, CATI project - email:
Ludovic Fillon - UPMC, CATI project - email:
Alexandre Routier - UPMC, CATI project - email:
François Touvet - INRIA - email:

Clinical Research Associates

Chabha Azouani - ICM, CATI project - email:
Xavier Badé - UPMC, CATI project - email:
Ali Bouyahia - ICM, CATI project - email:
Johanne Germain - UPMC, CATI project - email:

PhD students

Claire Cury - CNRS/UPMC - email:
Barbara Gris - ENS de Cachan - email:
Pietro Gori - INRIA/UPMC - email: pietro.gori [at] inria.fr
Takoua Kaaouana - UPMC, CATI project - email:
Jean-Baptiste Schiratti - École Polytechnique - email:

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Head

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:      -   Marie Chupin - CNRS Research Engineer (IR) - email:      -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Eric Bardinet - CNRS Research Engineer - email:
Sara Fernandez-Vidal - Inserm Research Engineer - email:
Laurent Hugueville - CNRS Engineer - email:
Denis Schwartz - Inserm Research Engineer - email:
Romain Valabrègue - Inserm Research Engineer - email:

Administrative assistants

Cindy Crossouard - INRIA - email
Thomas Estienne - UPMC, CATI project - email:
Corinne Omer - CNRS - email:

Postdocs

Yohan Attal - UPMC - email:
Fabrizio de Vico Fallani - IHU-A-ICM - email:
Ana Fouquier - CNRS - email:
Linda Marrakchi-Kacem - UPMC, CATI project - email:

Engineers

Hugo Dary - UPMC, CATI project - email:
Ludovic Fillon - UPMC, CATI project - email:
Alexandre Routier - UPMC, CATI project - email:
François Touvet - INRIA - email:

Clinical Research Associates

Chabha Azouani - ICM, CATI project - email:
Xavier Badé - UPMC, CATI project - email:
Ali Bouyahia - ICM, CATI project - email:
Johanne Germain - UPMC, CATI project - email:

PhD students

Claire Cury - CNRS/UPMC - email:
Barbara Gris - ENS de Cachan - email:
Pietro Gori - INRIA/UPMC - email: pietro.gori [at] inria.fr
Takoua Kaaouana - UPMC, CATI project - email:
Jean-Baptiste Schiratti - École Polytechnique - email:

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Head

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:      -   Marie Chupin - CNRS Research Engineer (IR) - email:      -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Eric Bardinet - CNRS Research Engineer - email:
Sara Fernandez-Vidal - Inserm Research Engineer - email:
Laurent Hugueville - CNRS Engineer - email:
Denis Schwartz - Inserm Research Engineer - email:
Romain Valabrègue - Inserm Research Engineer - email:

Administrative assistants

Cindy Crossouard - INRIA - email
Thomas Estienne - UPMC, CATI project - email:
Corinne Omer - CNRS - email:

Postdocs

Yohan Attal - UPMC - email:
Fabrizio de Vico Fallani - IHU-A-ICM - email:
Ana Fouquier - CNRS - email:
Linda Marrakchi-Kacem - UPMC, CATI project - email:

Engineers

Hugo Dary - UPMC, CATI project - email:
Ludovic Fillon - UPMC, CATI project - email:
Alexandre Routier - UPMC, CATI project - email:
François Touvet - INRIA - email:

Clinical Research Associates

Chabha Azouani - ICM, CATI project - email:
Xavier Badé - UPMC, CATI project - email:
Ali Bouyahia - ICM, CATI project - email:
Johanne Germain - UPMC, CATI project - email:

PhD students

Claire Cury - CNRS/UPMC - email:
Barbara Gris - ENS de Cachan - email:
Pietro Gori - INRIA/UPMC - email: pietro.gori [at] inria.fr
Takoua Kaaouana - UPMC, CATI project - email:
Jean-Baptiste Schiratti - École Polytechnique - email:

', 'Team Members', '', 'inherit', 'open', 'open', '', '4-revision-v1', '', '', '2014-03-03 19:13:01', '2014-03-03 18:13:01', '', 4, 'https://www.aramislab.fr/?p=173', 0, 'revision', '', (174, 1, '2014-03-03 19:15:59', '2014-03-03 18:15:59', '

Head

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:      -   Marie Chupin - CNRS Research Engineer (IR) - email:      -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Eric Bardinet - CNRS Research Engineer - email:
Sara Fernandez-Vidal - Inserm Research Engineer - email:
Laurent Hugueville - CNRS Engineer - email:
Denis Schwartz - Inserm Research Engineer - email:
Romain Valabrègue - Inserm Research Engineer - email:

Administrative assistants

Cindy Crossouard - INRIA - email
Thomas Estienne - UPMC, CATI project - email:
Corinne Omer - CNRS - email:

Postdocs

Yohan Attal - UPMC - email:
Fabrizio de Vico Fallani - IHU-A-ICM - email:
Ana Fouquier - CNRS - email:
Linda Marrakchi-Kacem - UPMC, CATI project - email:

Engineers

Hugo Dary - UPMC, CATI project - email:
Ludovic Fillon - UPMC, CATI project - email:
Alexandre Routier - UPMC, CATI project - email:
François Touvet - INRIA - email:

Clinical Research Associates

Chabha Azouani - ICM, CATI project - email:
Xavier Badé - UPMC, CATI project - email:
Ali Bouyahia - ICM, CATI project - email:
Johanne Germain - UPMC, CATI project - email:

PhD students

Claire Cury - CNRS/UPMC - email:
Barbara Gris - ENS de Cachan - email:
Pietro Gori - INRIA/UPMC - email: pietro.gori [at] inria.fr
Takoua Kaaouana - UPMC, CATI project - email:
Jean-Baptiste Schiratti - École Polytechnique - email:

', 'Team Members', '', 'inherit', 'open', 'open', '', '4-revision-v1', '', '', '2014-03-03 19:15:59', '2014-03-03 18:15:59', '', 4, 'https://www.aramislab.fr/?p=174', 0, 'revision', '', (175, 1, '2014-03-03 19:19:15', '2014-03-03 18:19:15', '

Head

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:      -   Marie Chupin - CNRS Research Engineer (IR) - email:      -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Eric Bardinet - CNRS Research Engineer - email:
Sara Fernandez-Vidal - Inserm Research Engineer - email:
Laurent Hugueville - CNRS Engineer - email:
Denis Schwartz - Inserm Research Engineer - email:
Romain Valabrègue - Inserm Research Engineer - email:

Administrative assistants

Cindy Crossouard - INRIA - email
Thomas Estienne - UPMC, CATI project - email:
Corinne Omer - CNRS - email:

Postdocs

Yohan Attal - UPMC - email:
Fabrizio de Vico Fallani - IHU-A-ICM - email:
Ana Fouquier - CNRS - email:
Linda Marrakchi-Kacem - UPMC, CATI project - email:

Engineers

Hugo Dary - UPMC, CATI project - email:
Ludovic Fillon - UPMC, CATI project - email:
Alexandre Routier - UPMC, CATI project - email:
François Touvet - INRIA - email:

Clinical Research Associates

Chabha Azouani - ICM, CATI project - email:
Xavier Badé - UPMC, CATI project - email:
Ali Bouyahia - ICM, CATI project - email:
Johanne Germain - UPMC, CATI project - email:

PhD students

Claire Cury - CNRS/UPMC - email:
Barbara Gris - ENS de Cachan - email:
Pietro Gori - INRIA/UPMC - email: pietro.gori [at] inria.fr
Takoua Kaaouana - UPMC, CATI project - email:
Jean-Baptiste Schiratti - École Polytechnique - email:

', 'Team Members', '', 'inherit', 'open', 'open', '', '4-revision-v1', '', '', '2014-03-03 19:19:15', '2014-03-03 18:19:15', '', 4, 'https://www.aramislab.fr/?p=175', 0, 'revision', '', (176, 1, '2014-03-03 19:21:01', '2014-03-03 18:21:01', '

Head

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:      -   Marie Chupin - CNRS Research Engineer (IR) - email:      -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Eric Bardinet - CNRS Research Engineer - email:
Sara Fernandez-Vidal - Inserm Research Engineer - email:
Laurent Hugueville - CNRS Engineer - email:
Denis Schwartz - Inserm Research Engineer - email:
Romain Valabrègue - Inserm Research Engineer - email:

Administrative assistants

Cindy Crossouard - INRIA - email
Thomas Estienne - UPMC, CATI project - email:
Corinne Omer - CNRS - email:

Postdocs

Yohan Attal - UPMC - email:
Fabrizio de Vico Fallani - IHU-A-ICM - email:
Ana Fouquier - CNRS - email:
Linda Marrakchi-Kacem - UPMC, CATI project - email:

Engineers

Hugo Dary - UPMC, CATI project - email:
Ludovic Fillon - UPMC, CATI project - email:
Alexandre Routier - UPMC, CATI project - email:
François Touvet - INRIA - email:

Clinical Research Associates

Chabha Azouani - ICM, CATI project - email:
Xavier Badé - UPMC, CATI project - email:
Ali Bouyahia - ICM, CATI project - email:
Johanne Germain - UPMC, CATI project - email:

PhD students

Claire Cury - CNRS/UPMC - email:
Barbara Gris - ENS de Cachan - email:
Pietro Gori - INRIA/UPMC - email: pietro.gori [at] inria.fr
Takoua Kaaouana - UPMC, CATI project - email:
Jean-Baptiste Schiratti - École Polytechnique - email:

', 'Team Members', '', 'inherit', 'open', 'open', '', '4-revision-v1', '', '', '2014-03-03 19:21:01', '2014-03-03 18:21:01', '', 4, 'https://www.aramislab.fr/?p=176', 0, 'revision', '', (177, 1, '2014-03-04 10:05:41', '2014-03-04 09:05:41', '

Ten most representative publications

https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_ref_biblio=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css

Publications from HAL

[HAL]https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_auteur=oui&CB_titre=oui&CB_article=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css[/HAL] [BASTRIEN] aramis [/BASTRIEN] [HAL] URL haltool [/HAL] [RAWEB] Nom équipe [/RAWEB] [TED] nom équipe [/TED]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-03-04 10:05:41', '2014-03-04 09:05:41', '', 26, 'https://www.aramislab.fr/?p=177', 0, 'revision', '', (178, 1, '2014-03-04 10:06:33', '2014-03-04 09:06:33', '

Ten most representative publications

 

Publications from HAL

[HAL]https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_auteur=oui&CB_titre=oui&CB_article=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css[/HAL] [BASTRIEN] aramis [/BASTRIEN] [RAWEB] aramis [/RAWEB] [TED] aramis [/TED]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-03-04 10:06:33', '2014-03-04 09:06:33', '', 26, 'https://www.aramislab.fr/?p=178', 0, 'revision', '', (179, 1, '2014-03-04 10:10:18', '2014-03-04 09:10:18', '

Ten most representative publications

 

Publications from HAL

[HAL]https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_auteur=oui&CB_titre=oui&CB_article=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css[/HAL]   [RAWEB] aramis [/RAWEB] [TED] aramis [/TED]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-03-04 10:10:18', '2014-03-04 09:10:18', '', 26, 'https://www.aramislab.fr/?p=179', 0, 'revision', '', (180, 1, '2014-03-04 10:20:11', '2014-03-04 09:20:11', '

Ten most representative publications

 

Publications from HAL

[HAL]https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_auteur=oui&CB_titre=oui&CB_article=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css[/HAL]   [TED] aramis [/TED]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-03-04 10:20:11', '2014-03-04 09:20:11', '', 26, 'https://www.aramislab.fr/?p=180', 0, 'revision', '', (181, 1, '2014-03-04 10:26:09', '2014-03-04 09:26:09', '

Ten most representative publications

 

Publications from HAL

[HAL]https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_auteur=oui&CB_titre=oui&CB_article=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css[/HAL]   [HAL]http://haltools.inria.fr/Public/afficheRequetePubli.php?annee_publideb=2011&annee_publifin=2011&labos_exp=planete&CB_auteur=oui&CB_titre=oui&CB_article=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=https://www.labri.fr/perso/pbenard/css/VisuGen.css[/HAL]   [TED] aramis [/TED]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-03-04 10:26:09', '2014-03-04 09:26:09', '', 26, 'https://www.aramislab.fr/?p=181', 0, 'revision', '', (182, 1, '2014-03-04 10:26:52', '2014-03-04 09:26:52', '

Ten most representative publications

 

Publications from HAL

[HAL]https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_auteur=oui&CB_titre=oui&CB_article=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css[/HAL]   [HAL]http://haltools.inria.fr/Public/afficheRequetePubli.php?annee_publideb=2011&annee_publifin=2011&labos_exp=planete&CB_auteur=oui&CB_titre=oui&CB_article=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=https://www.labri.fr/perso/pbenard/css/VisuGen.css[/HAL]   [TED] aramis [/TED]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-03-04 10:26:52', '2014-03-04 09:26:52', '', 26, 'https://www.aramislab.fr/?p=182', 0, 'revision', '', 0); INSERT INTO `wp_aramis_posts` VALUES (183, 1, '2014-03-04 10:34:01', '2014-03-04 09:34:01', '

Ten most representative publications

 

Publications from HAL

[HAL]https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_auteur=oui&CB_titre=oui&CB_article=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css[/HAL]   [HAL]http://haltools.inria.fr/Public/afficheRequetePubli.php?annee_publideb=2011&annee_publifin=2011&labos_exp=planete&CB_auteur=oui&CB_titre=oui&CB_article=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=https://www.labri.fr/perso/pbenard/css/VisuGen.css[/HAL]   [HAL]aramis[/HAL] [TED] aramis [/TED]', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-03-04 10:34:01', '2014-03-04 09:34:01', '', 26, 'https://www.aramislab.fr/?p=183', 0, 'revision', '', (184, 1, '2014-03-04 10:36:09', '2014-03-04 09:36:09', '

Ten most representative publications

 

Publications from HAL

[HAL]https://haltools.inria.fr/Public/afficheRequetePubli.php?labos_exp=aramis&CB_auteur=oui&CB_titre=oui&CB_article=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=../css/VisuRubriqueEncadre.css[/HAL]   [HAL]http://haltools.inria.fr/Public/afficheRequetePubli.php?annee_publideb=2011&annee_publifin=2011&labos_exp=planete&CB_auteur=oui&CB_titre=oui&CB_article=oui&langue=Francais&tri_exp=annee_publi&tri_exp2=typdoc&tri_exp3=date_publi&ordre_aff=TA&Fen=Aff&css=https://www.labri.fr/perso/pbenard/css/VisuGen.css[/HAL]   [HAL]aramis[/HAL] [TED]aramis[/TED] [BASTRIEN]aramis[/BASTRIEN] [RAWEB]aramis[/RAWEB]    ', 'Publications', '', 'inherit', 'open', 'open', '', '26-revision-v1', '', '', '2014-03-04 10:36:09', '2014-03-04 09:36:09', '', 26, 'https://www.aramislab.fr/?p=184', 0, 'revision', '', (185, 1, '2014-03-04 10:38:14', '2014-03-04 09:38:14', '

Head

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:      -   Marie Chupin - CNRS Research Engineer (IR) - email:      -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Eric Bardinet - CNRS Research Engineer - email:
Sara Fernandez-Vidal - Inserm Research Engineer - email:
Laurent Hugueville - CNRS Engineer - email:
Denis Schwartz - Inserm Research Engineer - email:
Romain Valabrègue - Inserm Research Engineer - email:

Administrative assistants

Cindy Crossouard - INRIA - email
Thomas Estienne - UPMC, CATI project - email:
Corinne Omer - CNRS - email:

Postdocs

Yohan Attal - UPMC - email:
Fabrizio de Vico Fallani - IHU-A-ICM - email:
Ana Fouquier - CNRS - email:
Linda Marrakchi-Kacem - UPMC, CATI project - email:

Engineers

Hugo Dary - UPMC, CATI project - email:
Ludovic Fillon - UPMC, CATI project - email:
Alexandre Routier - UPMC, CATI project - email:
François Touvet - INRIA - email:

Clinical Research Associates

Chabha Azouani - ICM, CATI project - email:
Xavier Badé - UPMC, CATI project - email:
Ali Bouyahia - ICM, CATI project - email:
Johanne Germain - UPMC, CATI project - email:

PhD students

Claire Cury - CNRS/UPMC - email:
Barbara Gris - ENS de Cachan - email:
Pietro Gori - INRIA/UPMC - email: pietro.gori [at] inria.fr
Takoua Kaaouana - UPMC, CATI project - email:
Jean-Baptiste Schiratti - École Polytechnique - email:

', 'Team Members', '', 'inherit', 'open', 'open', '', '4-revision-v1', '', '', '2014-03-04 10:38:14', '2014-03-04 09:38:14', '', 4, 'https://www.aramislab.fr/?p=185', 0, 'revision', '', (186, 1, '2014-03-04 10:41:30', '2014-03-04 09:41:30', '

Head

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:      -   Marie Chupin - CNRS Research Engineer (IR) - email:      -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Eric Bardinet - CNRS Research Engineer - email:
Sara Fernandez-Vidal - Inserm Research Engineer - email:
Laurent Hugueville - CNRS Engineer - email:
Denis Schwartz - Inserm Research Engineer - email:
Romain Valabrègue - Inserm Research Engineer - email:

Administrative assistants

Cindy Crossouard - INRIA - email
Thomas Estienne - UPMC, CATI project - email:
Corinne Omer - CNRS - email:

Postdocs

Yohan Attal - UPMC - email:
Fabrizio de Vico Fallani - IHU-A-ICM - email:
Ana Fouquier - CNRS - email:
Linda Marrakchi-Kacem - UPMC, CATI project - email:

Engineers

Hugo Dary - UPMC, CATI project - email:
Ludovic Fillon - UPMC, CATI project - email:
Alexandre Routier - UPMC, CATI project - email:
François Touvet - INRIA - email:

Clinical Research Associates

Chabha Azouani - ICM, CATI project - email:
Xavier Badé - UPMC, CATI project - email:
Ali Bouyahia - ICM, CATI project - email:
Johanne Germain - UPMC, CATI project - email:

PhD students

Claire Cury - CNRS/UPMC - email:
Barbara Gris - ENS de Cachan - email:
Pietro Gori - INRIA/UPMC - email: pietro.gori [at] inria.fr
Takoua Kaaouana - UPMC, CATI project - email:
Jean-Baptiste Schiratti - École Polytechnique - email:

', 'Team Members', '', 'inherit', 'open', 'open', '', '4-revision-v1', '', '', '2014-03-04 10:41:30', '2014-03-04 09:41:30', '', 4, 'https://www.aramislab.fr/?p=186', 0, 'revision', '', (187, 1, '2014-03-04 10:42:02', '2014-03-04 09:42:02', '

Head

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:      -   Marie Chupin - CNRS Research Engineer (IR) - email:      -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Eric Bardinet - CNRS Research Engineer - email:
Sara Fernandez-Vidal - Inserm Research Engineer - email:
Laurent Hugueville - CNRS Engineer - email:
Denis Schwartz - Inserm Research Engineer - email:
Romain Valabrègue - Inserm Research Engineer - email:

Administrative assistants

Cindy Crossouard - INRIA - email
Thomas Estienne - UPMC, CATI project - email:
Corinne Omer - CNRS - email:

Postdocs

Yohan Attal - UPMC - email:
Fabrizio de Vico Fallani - IHU-A-ICM - email:
Ana Fouquier - CNRS - email:
Linda Marrakchi-Kacem - UPMC, CATI project - email:

Engineers

Hugo Dary - UPMC, CATI project - email:
Ludovic Fillon - UPMC, CATI project - email:
Alexandre Routier - UPMC, CATI project - email:
François Touvet - INRIA - email:

Clinical Research Associates

Chabha Azouani - ICM, CATI project - email:
Xavier Badé - UPMC, CATI project - email:
Ali Bouyahia - ICM, CATI project - email:
Johanne Germain - UPMC, CATI project - email:

PhD students

Claire Cury - CNRS/UPMC - email:
Barbara Gris - ENS de Cachan - email:
Pietro Gori - INRIA/UPMC - email: pietro.gori [at] inria.fr
Takoua Kaaouana - UPMC, CATI project - email:
Jean-Baptiste Schiratti - École Polytechnique - email:

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Head

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:      -   Marie Chupin - CNRS Research Engineer (IR) - email:      -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Eric Bardinet - CNRS Research Engineer - email:
Sara Fernandez-Vidal - Inserm Research Engineer - email:
Laurent Hugueville - CNRS Engineer - email:
Denis Schwartz - Inserm Research Engineer - email:
Romain Valabrègue - Inserm Research Engineer - email:

Administrative assistants

Cindy Crossouard - INRIA - email
Thomas Estienne - UPMC, CATI project - email:
Corinne Omer - CNRS - email:

Postdocs

Yohan Attal - UPMC - email:
Fabrizio de Vico Fallani - IHU-A-ICM - email:
Ana Fouquier - CNRS - email:
Linda Marrakchi-Kacem - UPMC, CATI project - email:

Engineers

Hugo Dary - UPMC, CATI project - email:
Ludovic Fillon - UPMC, CATI project - email:
Alexandre Routier - UPMC, CATI project - email:
François Touvet - INRIA - email:

Clinical Research Associates

Chabha Azouani - ICM, CATI project - email:
Xavier Badé - UPMC, CATI project - email:
Ali Bouyahia - ICM, CATI project - email:
Johanne Germain - UPMC, CATI project - email:

PhD students

Claire Cury - CNRS/UPMC - email:
Barbara Gris - ENS de Cachan - email:
Pietro Gori - INRIA/UPMC - email: pietro.gori [at] inria.fr
Takoua Kaaouana - UPMC, CATI project - email:
Jean-Baptiste Schiratti - École Polytechnique - email:

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Head

Deputy Head

     -   Didier Dormont - Professor of Neuroradiology (PU-PH), University Pierre and Marie Curie / AP-HP - emaildidier.dormont [at] psl.aphp.fr

Researchers

     -   Mario Chavez- CNRS Researcher (CR1) - email:      -   Marie Chupin - CNRS Research Engineer (IR) - email:      -   Stanley Durrleman - INRIA Researcher - website - email

Medical Faculty / Clinicians

     -   Claude Adam - Neurologist (PH), AP-HP -email:
     -   Anne Bertrand - Neuroradiologist (CCA), AP-HP - email:
     -   Sophie Dupont - Professor of Neurology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Damien Galanaud - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), University Pierre and Marie Curie - email:
     -   Yves Samson - Professor of Neuroradiology (PU-PH),  University Pierre and Marie Curie / AP-HP - email:
     -   Lionel Thivard - Neurologist (PH), AP-HP - email:

Associated engineers of the neuroimaging core facility

Eric Bardinet - CNRS Research Engineer - email:
Sara Fernandez-Vidal - Inserm Research Engineer - email:
Laurent Hugueville - CNRS Engineer - email:
Denis Schwartz - Inserm Research Engineer - email:
Romain Valabrègue - Inserm Research Engineer - email:

Administrative assistants

Cindy Crossouard - INRIA - email
Thomas Estienne - UPMC, CATI project - email:
Corinne Omer - CNRS - email:

Postdocs

Yohan Attal - UPMC - email:
Fabrizio de Vico Fallani - IHU-A-ICM - email:
Ana Fouquier - CNRS - email:
Linda Marrakchi-Kacem - UPMC, CATI project - email:

Engineers

Hugo Dary - UPMC, CATI project - email:
Ludovic Fillon - UPMC, CATI project - email:
Alexandre Routier - UPMC, CATI project - email:
François Touvet - INRIA - email:

Clinical Research Associates

Chabha Azouani - ICM, CATI project - email:
Xavier Badé - UPMC, CATI project - email:
Ali Bouyahia - ICM, CATI project - email:
Johanne Germain - UPMC, CATI project - email:

PhD students

Claire Cury - CNRS/UPMC - email:
Barbara Gris - ENS de Cachan - email:
Pietro Gori - INRIA/UPMC - email: pietro.gori [at] inria.fr
Takoua Kaaouana - UPMC, CATI project - email:
Jean-Baptiste Schiratti - École Polytechnique - email:

', 'Team Members', '', 'inherit', 'open', 'open', '', '4-revision-v1', '', '', '2014-03-04 10:42:56', '2014-03-04 09:42:56', '', 4, 'https://www.aramislab.fr/?p=189', 0, 'revision', '', (190, 1, '2014-03-04 10:47:35', '2014-03-04 09:47:35', '

Head

Deputy Head

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2017-03-06 00:58:00', '2017-03-05 23:58:00', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1026, 7, '2017-03-06 00:58:20', '2017-03-05 23:58:20', '   New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.

  Network theoretic approaches to integrate heterogeneous brain netwo rks  

T

 

Spatio-temporal models to build trajectories of disease progression from longitudinal data  

Clinica

 

Deformetrica

References
  • S. Durrleman, S., Prastawa, M., Charon, N., Kore . Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 PDF Paper in PDF
  • Bône, A., Louis, M., Martin, B., & Durrleman, S. Deformetrica 4: an open-source software for statistical shape analysis. In nternational Workshop on Shape in Medical Imaging Springer, Cham, 2018. p. 3-13. PDF Paper in PDF
 

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.  
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Leaspy

To come soon References
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
 

Brain network toolbox

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download  

Clinica

Clinica For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica::
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-01-24 14:27:22', '2018-01-24 13:27:22', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1059, 7, '2018-01-24 14:28:50', '2018-01-24 13:28:50', '

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.  

Brain network toolbox

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download  

Clinica

Clinica For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica::
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-01-24 14:28:50', '2018-01-24 13:28:50', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1060, 7, '2018-01-24 14:29:32', '2018-01-24 13:29:32', '

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.  

Brain network toolbox

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download  

Clinica

Clinica For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica::
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-01-24 14:29:32', '2018-01-24 13:29:32', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1061, 7, '2018-01-24 14:30:30', '2018-01-24 13:30:30', '

Brain network toolbox

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download  

Clinica

Clinica For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica::
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
 

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014. ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-01-24 14:30:30', '2018-01-24 13:30:30', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1062, 7, '2018-01-24 14:33:03', '2018-01-24 13:33:03', '

Brain network toolbox

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download  

Clinica

Clinica For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr   Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
 

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-01-24 14:33:03', '2018-01-24 13:33:03', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1063, 7, '2018-01-24 14:33:26', '2018-01-24 13:33:26', '

Brain network toolbox

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download  

Clinica

Clinica For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:  
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
 

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-01-24 14:33:26', '2018-01-24 13:33:26', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1064, 7, '2018-01-24 14:34:38', '2018-01-24 13:34:38', '

Brain network toolbox

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download  

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:  
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
 

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-01-24 14:34:38', '2018-01-24 13:34:38', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1065, 7, '2018-01-24 14:35:27', '2018-01-24 13:35:27', '

Brain network toolbox

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download  

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:  
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
 

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-01-24 14:35:27', '2018-01-24 13:35:27', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1069, 6, '2018-03-06 11:17:44', '2018-03-06 10:17:44', ' If you are interested in joining the team (with respect to any of our research topics), do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).

Postdocs / Scientists

PhD thesis

Engineers / Software developers

 Master Internships / Stages de Master

  • See PhD thesis above
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Postdocs / Scientists

PhD thesis

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Postdocs / Scientists

PhD thesis

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Picture_Aramis

Direction

Researchers

Medical Faculty / Clinicians

Detached Researchers

Postdocs

PhD students

  • Manon Ansart
  • Alexandre Bône
  • Fanny Grosselin - grosselin.fanny@gmail.com
  • Jérémy Guillon - jeremy.guillon@inria.fr
  • Igor Koval
  • Maxime Louis
  • Pascal Lu
  • Catalina Obando Forero - catalina.obando@inria.fr
  • Alexandre Routier - alexandre.routier@upmc.fr
  • Jorge Samper Gonzales - jsampergonzalez@gmail.com
  • Wen Wei
  • Junhao Wen - junhao.wen@inria.fr

Associated collaborators

  • Stéphanie Allassonnière - École Polytechnique - webpage - stephanie.allassonniere@polytechnique.edu
  • Marie Chupin - CNRS Research Engineer (IR) - marie.chupin@upmc.fr

Former Members

  • Romain Valabrègue - Inserm Research Engineer - webpage - romain.valabregue@upmc.fr
  • Eric Bardinet - CNRS Research Engineer - webpage - eric.bardinet@upmc.fr
  • Sara Fernandez-Vidal - Inserm Research Engineer - webpage - sara.fernandez_vidal@upmc.fr
  • Vera Dinkelacker - Rothschild Foundation - v.dinkelacker@gmail.com
  • Thomas Estienne - UPMC, CATI project - thomas.estienne@icm-institute.org
  • Martina Sundqvist - Ecole Normale Supérieure de Paris
  • Thomas Jacquemont - Ecole Normale Supérieure de Paris
  • Aurore Bussalb - Institut Supérieur de BioSciences de Paris
  • Carlos Tor Diez - Télécom ParisTech
  • Ayoub Louati - Ecole Polytechnique de Tunisie
  • Paul Jusselin - Ecole Normale Supérieure de Cachan
  • Chabha Azouani - CATI project - chabha.azouani@upmc.fr
  • Kelly Martineau - CATI project – kelly.martineau@icm-institute.org
  • Sonia Djobeir - CATI project – soniadjobeir@gmail.com
  • Hugo Dary - CATI project - dary.hugo@gmail.com
  • Ludovic Fillon - CATI project - ludovic.fillon@upmc.fr
  • Mathieu Dubois - CATI project - mathieu.dubois@icm-institute.org
  • Barbara Gris - gris@cmla.ens-cachan.fr
  • Géraldine Rousseau - geraldine.rouseau@psl.ap-hop-paris.fr
  • Marika Rudler - marika.rudler@psl.aphp.fr
  • Jean-Baptiste Schiratti - jean-baptiste.schiratti@cmap.polytechnique.fr
  • Pietro Gori - Postdoctoral fellow - Now Assistant Professor, Telecom ParisTech
  • Ana Fouquier - - Postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Alexandre Pron - Master\'s student (Université Paris Descartes) - Now PhD student at INT, Marseille
  • Susovan Pal - Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Engineer - Now Pipeline TD Junior at Ellipse Studio
  • François Deloche - Intern (École Polytechnique)
  • Alexis Mocellin - Intern (Ecole Polytechnique)
  • Claire Cury - PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Clinical Research Associate - Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Clinical Research Associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Kanza Dekkiche - Master\'s student
  • Antoine Latrille - Master\'s student (ESIEE Paris) - Now Ingénieur Développeur Web at Easyvoyage
  • Guillaume Ruffin - Intern
  • Evgeny Zuenko - Master\'s student
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Maxime Corduant - Master\'s student
  • Kevin Roussel - Master\'s student
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP - claude.adam@psl.aphp.fr
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP - sophie.dupont@psl.aphp.fr
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP - damien.galanaud@icm-institute.org
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP - yves.samson@psl.aphp.fr
  • Lionel Thivard - Neurologist (PH), AP-HP - lionel.thivard@psl.aphp.fr
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Picture_Aramis

Direction

Researchers

Medical Faculty / Clinicians

Detached Researchers

Postdocs

PhD students

  • Manon Ansart
  • Alexandre Bône
  • Fanny Grosselin - grosselin.fanny@gmail.com
  • Jérémy Guillon - jeremy.guillon@inria.fr
  • Igor Koval
  • Maxime Louis
  • Pascal Lu
  • Catalina Obando Forero - catalina.obando@inria.fr
  • Alexandre Routier - alexandre.routier@upmc.fr - webpage
  • Jorge Samper Gonzales - jsampergonzalez@gmail.com
  • Wen Wei
  • Junhao Wen - junhao.wen@inria.fr

Associated collaborators

  • Stéphanie Allassonnière - École Polytechnique - webpage - stephanie.allassonniere@polytechnique.edu
  • Marie Chupin - CNRS Research Engineer (IR) - marie.chupin@upmc.fr

Former Members

  • Romain Valabrègue - Inserm Research Engineer - webpage - romain.valabregue@upmc.fr
  • Eric Bardinet - CNRS Research Engineer - webpage - eric.bardinet@upmc.fr
  • Sara Fernandez-Vidal - Inserm Research Engineer - webpage - sara.fernandez_vidal@upmc.fr
  • Vera Dinkelacker - Rothschild Foundation - v.dinkelacker@gmail.com
  • Thomas Estienne - UPMC, CATI project - thomas.estienne@icm-institute.org
  • Martina Sundqvist - Ecole Normale Supérieure de Paris
  • Thomas Jacquemont - Ecole Normale Supérieure de Paris
  • Aurore Bussalb - Institut Supérieur de BioSciences de Paris
  • Carlos Tor Diez - Télécom ParisTech
  • Ayoub Louati - Ecole Polytechnique de Tunisie
  • Paul Jusselin - Ecole Normale Supérieure de Cachan
  • Chabha Azouani - CATI project - chabha.azouani@upmc.fr
  • Kelly Martineau - CATI project – kelly.martineau@icm-institute.org
  • Sonia Djobeir - CATI project – soniadjobeir@gmail.com
  • Hugo Dary - CATI project - dary.hugo@gmail.com
  • Ludovic Fillon - CATI project - ludovic.fillon@upmc.fr
  • Mathieu Dubois - CATI project - mathieu.dubois@icm-institute.org
  • Barbara Gris - gris@cmla.ens-cachan.fr
  • Géraldine Rousseau - geraldine.rouseau@psl.ap-hop-paris.fr
  • Marika Rudler - marika.rudler@psl.aphp.fr
  • Jean-Baptiste Schiratti - jean-baptiste.schiratti@cmap.polytechnique.fr
  • Pietro Gori - Postdoctoral fellow - Now Assistant Professor, Telecom ParisTech
  • Ana Fouquier - - Postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Alexandre Pron - Master\'s student (Université Paris Descartes) - Now PhD student at INT, Marseille
  • Susovan Pal - Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Engineer - Now Pipeline TD Junior at Ellipse Studio
  • François Deloche - Intern (École Polytechnique)
  • Alexis Mocellin - Intern (Ecole Polytechnique)
  • Claire Cury - PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Clinical Research Associate - Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Clinical Research Associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Kanza Dekkiche - Master\'s student
  • Antoine Latrille - Master\'s student (ESIEE Paris) - Now Ingénieur Développeur Web at Easyvoyage
  • Guillaume Ruffin - Intern
  • Evgeny Zuenko - Master\'s student
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Maxime Corduant - Master\'s student
  • Kevin Roussel - Master\'s student
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP - claude.adam@psl.aphp.fr
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP - sophie.dupont@psl.aphp.fr
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP - damien.galanaud@icm-institute.org
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP - yves.samson@psl.aphp.fr
  • Lionel Thivard - Neurologist (PH), AP-HP - lionel.thivard@psl.aphp.fr
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Postdocs / Scientists

PhD thesis

Engineers / Software developers

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Postdocs / Scientists

PhD thesis

Engineers / Software developers

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Postdocs / Scientists

PhD thesis

Engineers / Software developers

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The Aramis Lab brings together methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numerical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University and belongs to the Paris Brain Institute (ICM), which is a neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
We are financially supported by these institutions.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical learning, Deep learning, Generative models, Bayesian models [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Magnetic resonance Imaging (MRI), Positron Emission Tomography (PET)[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Riemannian Geometry, Diffeomorphisms[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Graph analysis, Multilayer networks, Network dynamics[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Non-linear mixed effects models, Riemannian geometry, Longitudinal shape models[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Clinical decision support systems, Disease progression models, Network alterations[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Modeling the presymptomatic phase in genetic forms of the disease[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Using multimodal neuroimaging to track disease progression[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Modeling disease progression, Computer-aided adjustment of treatment[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Using BCI for rehabilitation, BCI systems based on network analysis[/su_spoiler] [/su_accordion]
 
Team retreat at the villa Finaly, Florence, Italy
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Aramis

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Aramis

AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.     ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.   Key methodological domains :
  • Statistical and machine learning
  • Medical image processing
  • Morphometry, statistical shape analysis
  • Complex networks theory
  • Graph analysis
  • Longitudinal models
  Main applications :
  • Alzheimer’s disease
  • Fronto-temporal dementia
  • Multiple sclerosis
  • Parkinson\'s disease
  • Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Homebis', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 16:50:34', '2018-11-16 15:50:34', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1118, 7, '2018-11-16 16:50:39', '2018-11-16 15:50:39', '

Aramis

AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.     ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.   Key methodological domains :
  • Statistical and machine learning
  • Medical image processing
  • Morphometry, statistical shape analysis
  • Complex networks theory
  • Graph analysis
  • Longitudinal models
  Main applications :
  • Alzheimer’s disease
  • Fronto-temporal dementia
  • Multiple sclerosis
  • Parkinson\'s disease
  • Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 16:50:39', '2018-11-16 15:50:39', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1122, 7, '2018-11-16 16:54:56', '2018-11-16 15:54:56', '

Brain network toolbox

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download  

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:  
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
 

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 16:54:56', '2018-11-16 15:54:56', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1126, 7, '2018-11-16 16:57:28', '2018-11-16 15:57:28', '

Aramis

AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.     ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.   Key methodological domains :
  • Statistical and machine learning
  • Medical image processing
  • Morphometry, statistical shape analysis
  • Complex networks theory
  • Graph analysis
  • Longitudinal models
  Main applications :
  • Alzheimer’s disease
  • Fronto-temporal dementia
  • Multiple sclerosis
  • Parkinson\'s disease
  • Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. 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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques em that learn regularities in data to better alleviate similarities or differences [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
  [caption id="attachment_1227" align="center"]
Team retreat at the Villa Finaly, Florence, Italy
[/caption]
Team retreat at the villa Finaly, Florence, Italy
', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-autosave-v1', '', '', '2018-11-19 12:19:52', '2018-11-19 11:19:52', '', 1098, 'https://www.aramislab.fr/1098-autosave-v1/', 0, 'revision', '', (1192, 7, '2018-11-16 17:55:11', '2018-11-16 16:55:11', '[su_slider source="logo_ARAMISLAB_rvb clinica_icon_flat" limit="20" link="none" target="self" width="600" height="300" responsive="yes" title="yes" centered="yes" arrows="yes" pages="yes" mousewheel="yes" autoplay="5000" speed="600" class=""] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.     ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.   Key methodological domains :
  • Statistical and machine learning
  • Medical image processing
  • Morphometry, statistical shape analysis
  • Complex networks theory
  • Graph analysis
  • Longitudinal models
  Main applications :
  • Alzheimer’s disease
  • Fronto-temporal dementia
  • Multiple sclerosis
  • Parkinson\'s disease
  • Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 17:55:11', '2018-11-16 16:55:11', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1193, 7, '2018-11-16 17:57:01', '2018-11-16 16:57:01', '[su_slider source="media: 985,982,971,968,966"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.     ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.   Key methodological domains :
  • Statistical and machine learning
  • Medical image processing
  • Morphometry, statistical shape analysis
  • Complex networks theory
  • Graph analysis
  • Longitudinal models
  Main applications :
  • Alzheimer’s disease
  • Fronto-temporal dementia
  • Multiple sclerosis
  • Parkinson\'s disease
  • Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 17:57:01', '2018-11-16 16:57:01', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1194, 7, '2018-11-16 17:57:56', '2018-11-16 16:57:56', '[su_slider source="media: 985,982,971,968,966" height="200"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.     ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.   Key methodological domains :
  • Statistical and machine learning
  • Medical image processing
  • Morphometry, statistical shape analysis
  • Complex networks theory
  • Graph analysis
  • Longitudinal models
  Main applications :
  • Alzheimer’s disease
  • Fronto-temporal dementia
  • Multiple sclerosis
  • Parkinson\'s disease
  • Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 17:57:56', '2018-11-16 16:57:56', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1195, 7, '2018-11-16 18:03:33', '2018-11-16 17:03:33', '

Faculty

[tmm name="faculty"]

Picture_Aramis

Direction

Researchers

Medical Faculty / Clinicians

Detached Researchers

Postdocs

PhD students

  • Manon Ansart
  • Alexandre Bône
  • Fanny Grosselin - grosselin.fanny@gmail.com
  • Jérémy Guillon - jeremy.guillon@inria.fr
  • Igor Koval
  • Maxime Louis
  • Pascal Lu
  • Catalina Obando Forero - catalina.obando@inria.fr
  • Alexandre Routier - alexandre.routier@upmc.fr - webpage
  • Jorge Samper Gonzales - jsampergonzalez@gmail.com
  • Wen Wei
  • Junhao Wen - junhao.wen@inria.fr

Associated collaborators

  • Stéphanie Allassonnière - École Polytechnique - webpage - stephanie.allassonniere@polytechnique.edu
  • Marie Chupin - CNRS Research Engineer (IR) - marie.chupin@upmc.fr

Former Members

  • Romain Valabrègue - Inserm Research Engineer - webpage - romain.valabregue@upmc.fr
  • Eric Bardinet - CNRS Research Engineer - webpage - eric.bardinet@upmc.fr
  • Sara Fernandez-Vidal - Inserm Research Engineer - webpage - sara.fernandez_vidal@upmc.fr
  • Vera Dinkelacker - Rothschild Foundation - v.dinkelacker@gmail.com
  • Thomas Estienne - UPMC, CATI project - thomas.estienne@icm-institute.org
  • Martina Sundqvist - Ecole Normale Supérieure de Paris
  • Thomas Jacquemont - Ecole Normale Supérieure de Paris
  • Aurore Bussalb - Institut Supérieur de BioSciences de Paris
  • Carlos Tor Diez - Télécom ParisTech
  • Ayoub Louati - Ecole Polytechnique de Tunisie
  • Paul Jusselin - Ecole Normale Supérieure de Cachan
  • Chabha Azouani - CATI project - chabha.azouani@upmc.fr
  • Kelly Martineau - CATI project – kelly.martineau@icm-institute.org
  • Sonia Djobeir - CATI project – soniadjobeir@gmail.com
  • Hugo Dary - CATI project - dary.hugo@gmail.com
  • Ludovic Fillon - CATI project - ludovic.fillon@upmc.fr
  • Mathieu Dubois - CATI project - mathieu.dubois@icm-institute.org
  • Barbara Gris - gris@cmla.ens-cachan.fr
  • Géraldine Rousseau - geraldine.rouseau@psl.ap-hop-paris.fr
  • Marika Rudler - marika.rudler@psl.aphp.fr
  • Jean-Baptiste Schiratti - jean-baptiste.schiratti@cmap.polytechnique.fr
  • Pietro Gori - Postdoctoral fellow - Now Assistant Professor, Telecom ParisTech
  • Ana Fouquier - - Postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Alexandre Pron - Master\'s student (Université Paris Descartes) - Now PhD student at INT, Marseille
  • Susovan Pal - Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Engineer - Now Pipeline TD Junior at Ellipse Studio
  • François Deloche - Intern (École Polytechnique)
  • Alexis Mocellin - Intern (Ecole Polytechnique)
  • Claire Cury - PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Clinical Research Associate - Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Clinical Research Associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Kanza Dekkiche - Master\'s student
  • Antoine Latrille - Master\'s student (ESIEE Paris) - Now Ingénieur Développeur Web at Easyvoyage
  • Guillaume Ruffin - Intern
  • Evgeny Zuenko - Master\'s student
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Maxime Corduant - Master\'s student
  • Kevin Roussel - Master\'s student
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP - claude.adam@psl.aphp.fr
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP - sophie.dupont@psl.aphp.fr
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP - damien.galanaud@icm-institute.org
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP - yves.samson@psl.aphp.fr
  • Lionel Thivard - Neurologist (PH), AP-HP - lionel.thivard@psl.aphp.fr
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2018-11-16 18:03:33', '2018-11-16 17:03:33', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '', (1196, 7, '2018-11-16 18:03:49', '2018-11-16 17:03:49', '

Faculty

[tmm name="faculty"]

Researchers

Medical Faculty / Clinicians

Detached Researchers

Postdocs

PhD students

  • Manon Ansart
  • Alexandre Bône
  • Fanny Grosselin - grosselin.fanny@gmail.com
  • Jérémy Guillon - jeremy.guillon@inria.fr
  • Igor Koval
  • Maxime Louis
  • Pascal Lu
  • Catalina Obando Forero - catalina.obando@inria.fr
  • Alexandre Routier - alexandre.routier@upmc.fr - webpage
  • Jorge Samper Gonzales - jsampergonzalez@gmail.com
  • Wen Wei
  • Junhao Wen - junhao.wen@inria.fr

Associated collaborators

  • Stéphanie Allassonnière - École Polytechnique - webpage - stephanie.allassonniere@polytechnique.edu
  • Marie Chupin - CNRS Research Engineer (IR) - marie.chupin@upmc.fr

Former Members

  • Romain Valabrègue - Inserm Research Engineer - webpage - romain.valabregue@upmc.fr
  • Eric Bardinet - CNRS Research Engineer - webpage - eric.bardinet@upmc.fr
  • Sara Fernandez-Vidal - Inserm Research Engineer - webpage - sara.fernandez_vidal@upmc.fr
  • Vera Dinkelacker - Rothschild Foundation - v.dinkelacker@gmail.com
  • Thomas Estienne - UPMC, CATI project - thomas.estienne@icm-institute.org
  • Martina Sundqvist - Ecole Normale Supérieure de Paris
  • Thomas Jacquemont - Ecole Normale Supérieure de Paris
  • Aurore Bussalb - Institut Supérieur de BioSciences de Paris
  • Carlos Tor Diez - Télécom ParisTech
  • Ayoub Louati - Ecole Polytechnique de Tunisie
  • Paul Jusselin - Ecole Normale Supérieure de Cachan
  • Chabha Azouani - CATI project - chabha.azouani@upmc.fr
  • Kelly Martineau - CATI project – kelly.martineau@icm-institute.org
  • Sonia Djobeir - CATI project – soniadjobeir@gmail.com
  • Hugo Dary - CATI project - dary.hugo@gmail.com
  • Ludovic Fillon - CATI project - ludovic.fillon@upmc.fr
  • Mathieu Dubois - CATI project - mathieu.dubois@icm-institute.org
  • Barbara Gris - gris@cmla.ens-cachan.fr
  • Géraldine Rousseau - geraldine.rouseau@psl.ap-hop-paris.fr
  • Marika Rudler - marika.rudler@psl.aphp.fr
  • Jean-Baptiste Schiratti - jean-baptiste.schiratti@cmap.polytechnique.fr
  • Pietro Gori - Postdoctoral fellow - Now Assistant Professor, Telecom ParisTech
  • Ana Fouquier - - Postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Alexandre Pron - Master\'s student (Université Paris Descartes) - Now PhD student at INT, Marseille
  • Susovan Pal - Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Engineer - Now Pipeline TD Junior at Ellipse Studio
  • François Deloche - Intern (École Polytechnique)
  • Alexis Mocellin - Intern (Ecole Polytechnique)
  • Claire Cury - PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Clinical Research Associate - Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Clinical Research Associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Kanza Dekkiche - Master\'s student
  • Antoine Latrille - Master\'s student (ESIEE Paris) - Now Ingénieur Développeur Web at Easyvoyage
  • Guillaume Ruffin - Intern
  • Evgeny Zuenko - Master\'s student
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Maxime Corduant - Master\'s student
  • Kevin Roussel - Master\'s student
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP - claude.adam@psl.aphp.fr
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP - sophie.dupont@psl.aphp.fr
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP - damien.galanaud@icm-institute.org
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP - yves.samson@psl.aphp.fr
  • Lionel Thivard - Neurologist (PH), AP-HP - lionel.thivard@psl.aphp.fr
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2018-11-16 18:03:49', '2018-11-16 17:03:49', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '', (1197, 7, '2018-11-16 18:04:10', '2018-11-16 17:04:10', '

Faculty

[tmm name="faculty"]

Researchers

Medical Faculty / Clinicians

Detached Researchers

Postdocs

PhD students

  • Manon Ansart
  • Alexandre Bône
  • Fanny Grosselin - grosselin.fanny@gmail.com
  • Jérémy Guillon - jeremy.guillon@inria.fr
  • Igor Koval
  • Maxime Louis
  • Pascal Lu
  • Catalina Obando Forero - catalina.obando@inria.fr
  • Alexandre Routier - alexandre.routier@upmc.fr - webpage
  • Jorge Samper Gonzales - jsampergonzalez@gmail.com
  • Wen Wei
  • Junhao Wen - junhao.wen@inria.fr

Associated collaborators

  • Stéphanie Allassonnière - École Polytechnique - webpage - stephanie.allassonniere@polytechnique.edu
  • Marie Chupin - CNRS Research Engineer (IR) - marie.chupin@upmc.fr

Former Members

  • Romain Valabrègue - Inserm Research Engineer - webpage - romain.valabregue@upmc.fr
  • Eric Bardinet - CNRS Research Engineer - webpage - eric.bardinet@upmc.fr
  • Sara Fernandez-Vidal - Inserm Research Engineer - webpage - sara.fernandez_vidal@upmc.fr
  • Vera Dinkelacker - Rothschild Foundation - v.dinkelacker@gmail.com
  • Thomas Estienne - UPMC, CATI project - thomas.estienne@icm-institute.org
  • Martina Sundqvist - Ecole Normale Supérieure de Paris
  • Thomas Jacquemont - Ecole Normale Supérieure de Paris
  • Aurore Bussalb - Institut Supérieur de BioSciences de Paris
  • Carlos Tor Diez - Télécom ParisTech
  • Ayoub Louati - Ecole Polytechnique de Tunisie
  • Paul Jusselin - Ecole Normale Supérieure de Cachan
  • Chabha Azouani - CATI project - chabha.azouani@upmc.fr
  • Kelly Martineau - CATI project – kelly.martineau@icm-institute.org
  • Sonia Djobeir - CATI project – soniadjobeir@gmail.com
  • Hugo Dary - CATI project - dary.hugo@gmail.com
  • Ludovic Fillon - CATI project - ludovic.fillon@upmc.fr
  • Mathieu Dubois - CATI project - mathieu.dubois@icm-institute.org
  • Barbara Gris - gris@cmla.ens-cachan.fr
  • Géraldine Rousseau - geraldine.rouseau@psl.ap-hop-paris.fr
  • Marika Rudler - marika.rudler@psl.aphp.fr
  • Jean-Baptiste Schiratti - jean-baptiste.schiratti@cmap.polytechnique.fr
  • Pietro Gori - Postdoctoral fellow - Now Assistant Professor, Telecom ParisTech
  • Ana Fouquier - - Postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Alexandre Pron - Master\'s student (Université Paris Descartes) - Now PhD student at INT, Marseille
  • Susovan Pal - Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Engineer - Now Pipeline TD Junior at Ellipse Studio
  • François Deloche - Intern (École Polytechnique)
  • Alexis Mocellin - Intern (Ecole Polytechnique)
  • Claire Cury - PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Clinical Research Associate - Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Clinical Research Associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Kanza Dekkiche - Master\'s student
  • Antoine Latrille - Master\'s student (ESIEE Paris) - Now Ingénieur Développeur Web at Easyvoyage
  • Guillaume Ruffin - Intern
  • Evgeny Zuenko - Master\'s student
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Maxime Corduant - Master\'s student
  • Kevin Roussel - Master\'s student
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP - claude.adam@psl.aphp.fr
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP - sophie.dupont@psl.aphp.fr
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP - damien.galanaud@icm-institute.org
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP - yves.samson@psl.aphp.fr
  • Lionel Thivard - Neurologist (PH), AP-HP - lionel.thivard@psl.aphp.fr
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2018-11-16 18:04:10', '2018-11-16 17:04:10', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '', (1198, 7, '2018-11-16 18:05:28', '2018-11-16 17:05:28', '

Faculty

[tmm name="faculty"]

Collaborators

[tmm name="collaborators"]

Post-docs

[tmm name="post-docs"]

Engineers

[tmm name="engineers"]

PhD Students

[tmm name="phd-students"]

Alumni

[tmm name="alumni"]

Researchers

Medical Faculty / Clinicians

Detached Researchers

Postdocs

PhD students

  • Manon Ansart
  • Alexandre Bône
  • Fanny Grosselin - grosselin.fanny@gmail.com
  • Jérémy Guillon - jeremy.guillon@inria.fr
  • Igor Koval
  • Maxime Louis
  • Pascal Lu
  • Catalina Obando Forero - catalina.obando@inria.fr
  • Alexandre Routier - alexandre.routier@upmc.fr - webpage
  • Jorge Samper Gonzales - jsampergonzalez@gmail.com
  • Wen Wei
  • Junhao Wen - junhao.wen@inria.fr

Associated collaborators

  • Stéphanie Allassonnière - École Polytechnique - webpage - stephanie.allassonniere@polytechnique.edu
  • Marie Chupin - CNRS Research Engineer (IR) - marie.chupin@upmc.fr

Former Members

  • Romain Valabrègue - Inserm Research Engineer - webpage - romain.valabregue@upmc.fr
  • Eric Bardinet - CNRS Research Engineer - webpage - eric.bardinet@upmc.fr
  • Sara Fernandez-Vidal - Inserm Research Engineer - webpage - sara.fernandez_vidal@upmc.fr
  • Vera Dinkelacker - Rothschild Foundation - v.dinkelacker@gmail.com
  • Thomas Estienne - UPMC, CATI project - thomas.estienne@icm-institute.org
  • Martina Sundqvist - Ecole Normale Supérieure de Paris
  • Thomas Jacquemont - Ecole Normale Supérieure de Paris
  • Aurore Bussalb - Institut Supérieur de BioSciences de Paris
  • Carlos Tor Diez - Télécom ParisTech
  • Ayoub Louati - Ecole Polytechnique de Tunisie
  • Paul Jusselin - Ecole Normale Supérieure de Cachan
  • Chabha Azouani - CATI project - chabha.azouani@upmc.fr
  • Kelly Martineau - CATI project – kelly.martineau@icm-institute.org
  • Sonia Djobeir - CATI project – soniadjobeir@gmail.com
  • Hugo Dary - CATI project - dary.hugo@gmail.com
  • Ludovic Fillon - CATI project - ludovic.fillon@upmc.fr
  • Mathieu Dubois - CATI project - mathieu.dubois@icm-institute.org
  • Barbara Gris - gris@cmla.ens-cachan.fr
  • Géraldine Rousseau - geraldine.rouseau@psl.ap-hop-paris.fr
  • Marika Rudler - marika.rudler@psl.aphp.fr
  • Jean-Baptiste Schiratti - jean-baptiste.schiratti@cmap.polytechnique.fr
  • Pietro Gori - Postdoctoral fellow - Now Assistant Professor, Telecom ParisTech
  • Ana Fouquier - - Postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Alexandre Pron - Master\'s student (Université Paris Descartes) - Now PhD student at INT, Marseille
  • Susovan Pal - Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Engineer - Now Pipeline TD Junior at Ellipse Studio
  • François Deloche - Intern (École Polytechnique)
  • Alexis Mocellin - Intern (Ecole Polytechnique)
  • Claire Cury - PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Clinical Research Associate - Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Clinical Research Associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Kanza Dekkiche - Master\'s student
  • Antoine Latrille - Master\'s student (ESIEE Paris) - Now Ingénieur Développeur Web at Easyvoyage
  • Guillaume Ruffin - Intern
  • Evgeny Zuenko - Master\'s student
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Maxime Corduant - Master\'s student
  • Kevin Roussel - Master\'s student
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP - claude.adam@psl.aphp.fr
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP - sophie.dupont@psl.aphp.fr
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP - damien.galanaud@icm-institute.org
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP - yves.samson@psl.aphp.fr
  • Lionel Thivard - Neurologist (PH), AP-HP - lionel.thivard@psl.aphp.fr
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2018-11-16 18:05:28', '2018-11-16 17:05:28', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '', (1200, 7, '2018-11-16 18:07:27', '2018-11-16 17:07:27', '

Faculty

[tmm name="faculty"]

Collaborators

[tmm name="collaborators"]

Post-docs

[tmm name="post-docs"]

Engineers

[tmm name="engineers"]

PhD Students

[tmm name="phd-students"]

Master students

[tmm name="master-students"]

Alumni

[tmm name="alumni"]

Researchers

Medical Faculty / Clinicians

Detached Researchers

Postdocs

PhD students

  • Manon Ansart
  • Alexandre Bône
  • Fanny Grosselin - grosselin.fanny@gmail.com
  • Jérémy Guillon - jeremy.guillon@inria.fr
  • Igor Koval
  • Maxime Louis
  • Pascal Lu
  • Catalina Obando Forero - catalina.obando@inria.fr
  • Alexandre Routier - alexandre.routier@upmc.fr - webpage
  • Jorge Samper Gonzales - jsampergonzalez@gmail.com
  • Wen Wei
  • Junhao Wen - junhao.wen@inria.fr

Associated collaborators

  • Stéphanie Allassonnière - École Polytechnique - webpage - stephanie.allassonniere@polytechnique.edu
  • Marie Chupin - CNRS Research Engineer (IR) - marie.chupin@upmc.fr

Former Members

  • Romain Valabrègue - Inserm Research Engineer - webpage - romain.valabregue@upmc.fr
  • Eric Bardinet - CNRS Research Engineer - webpage - eric.bardinet@upmc.fr
  • Sara Fernandez-Vidal - Inserm Research Engineer - webpage - sara.fernandez_vidal@upmc.fr
  • Vera Dinkelacker - Rothschild Foundation - v.dinkelacker@gmail.com
  • Thomas Estienne - UPMC, CATI project - thomas.estienne@icm-institute.org
  • Martina Sundqvist - Ecole Normale Supérieure de Paris
  • Thomas Jacquemont - Ecole Normale Supérieure de Paris
  • Aurore Bussalb - Institut Supérieur de BioSciences de Paris
  • Carlos Tor Diez - Télécom ParisTech
  • Ayoub Louati - Ecole Polytechnique de Tunisie
  • Paul Jusselin - Ecole Normale Supérieure de Cachan
  • Chabha Azouani - CATI project - chabha.azouani@upmc.fr
  • Kelly Martineau - CATI project – kelly.martineau@icm-institute.org
  • Sonia Djobeir - CATI project – soniadjobeir@gmail.com
  • Hugo Dary - CATI project - dary.hugo@gmail.com
  • Ludovic Fillon - CATI project - ludovic.fillon@upmc.fr
  • Mathieu Dubois - CATI project - mathieu.dubois@icm-institute.org
  • Barbara Gris - gris@cmla.ens-cachan.fr
  • Géraldine Rousseau - geraldine.rouseau@psl.ap-hop-paris.fr
  • Marika Rudler - marika.rudler@psl.aphp.fr
  • Jean-Baptiste Schiratti - jean-baptiste.schiratti@cmap.polytechnique.fr
  • Pietro Gori - Postdoctoral fellow - Now Assistant Professor, Telecom ParisTech
  • Ana Fouquier - - Postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Alexandre Pron - Master\'s student (Université Paris Descartes) - Now PhD student at INT, Marseille
  • Susovan Pal - Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Engineer - Now Pipeline TD Junior at Ellipse Studio
  • François Deloche - Intern (École Polytechnique)
  • Alexis Mocellin - Intern (Ecole Polytechnique)
  • Claire Cury - PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Clinical Research Associate - Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Clinical Research Associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Kanza Dekkiche - Master\'s student
  • Antoine Latrille - Master\'s student (ESIEE Paris) - Now Ingénieur Développeur Web at Easyvoyage
  • Guillaume Ruffin - Intern
  • Evgeny Zuenko - Master\'s student
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Maxime Corduant - Master\'s student
  • Kevin Roussel - Master\'s student
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP - claude.adam@psl.aphp.fr
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP - sophie.dupont@psl.aphp.fr
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP - damien.galanaud@icm-institute.org
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP - yves.samson@psl.aphp.fr
  • Lionel Thivard - Neurologist (PH), AP-HP - lionel.thivard@psl.aphp.fr
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2018-11-16 18:07:27', '2018-11-16 17:07:27', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '', (1201, 7, '2018-11-16 18:24:37', '2018-11-16 17:24:37', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.     ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.   Key methodological domains :
  • Statistical and machine learning
  • Medical image processing
  • Morphometry, statistical shape analysis
  • Complex networks theory
  • Graph analysis
  • Longitudinal models
  Main applications :
  • Alzheimer’s disease
  • Fronto-temporal dementia
  • Multiple sclerosis
  • Parkinson\'s disease
  • Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:24:37', '2018-11-16 17:24:37', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1202, 7, '2018-11-16 18:25:40', '2018-11-16 17:25:40', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital. [su_label type="info"]Statistical and machine learning[/su_label] Key methodological domains :
  • Statistical and machine learning
  • Medical image processing
  • Morphometry, statistical shape analysis
  • Complex networks theory
  • Graph analysis
  • Longitudinal models
  Main applications :
  • Alzheimer’s disease
  • Fronto-temporal dementia
  • Multiple sclerosis
  • Parkinson\'s disease
  • Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:25:40', '2018-11-16 17:25:40', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1203, 7, '2018-11-16 18:26:33', '2018-11-16 17:26:33', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital. [su_label type="info"]Statistical and machine learning[/su_label] [su_label type="info"]Medical image processing[/su_label] Key methodological domains :
  • Statistical and machine learning
  • Medical image processing
  • Morphometry, statistical shape analysis
  • Complex networks theory
  • Graph analysis
  • Longitudinal models
  Main applications :
  • Alzheimer’s disease
  • Fronto-temporal dementia
  • Multiple sclerosis
  • Parkinson\'s disease
  • Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:26:33', '2018-11-16 17:26:33', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1204, 7, '2018-11-16 18:28:20', '2018-11-16 17:28:20', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

[su_label type="info"]Statistical and machine learning[/su_label] [su_label type="info"]Medical image processing[/su_label] [su_label type="info"]Morphometry, statistical shape analysis[/su_label] [su_label type="info"]Complex networks theory[/su_label] [su_label type="info"]Graph analysis[/su_label] [su_label type="info"]Longitudinal models[/su_label]

Main applications

Main applications :
  • Alzheimer’s disease
  • Fronto-temporal dementia
  • Multiple sclerosis
  • Parkinson\'s disease
  • Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:28:20', '2018-11-16 17:28:20', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1205, 7, '2018-11-16 18:28:44', '2018-11-16 17:28:44', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

[su_label type="info"]Statistical and machine learning[/su_label] [su_label type="info"]Medical image processing[/su_label] [su_label type="info"]Morphometry, statistical shape analysis[/su_label] [su_label type="info"]Complex networks theory[/su_label] [su_label type="info"]Graph analysis[/su_label] [su_label type="info"]Longitudinal models[/su_label]

Main applications

Main applications :
  • Alzheimer’s disease
  • Fronto-temporal dementia
  • Multiple sclerosis
  • Parkinson\'s disease
  • Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:28:44', '2018-11-16 17:28:44', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1206, 7, '2018-11-16 18:29:38', '2018-11-16 17:29:38', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

[su_label type="info"]Statistical and machine learning[/su_label] [su_label type="info"]Medical image processing[/su_label] [su_label type="info"]Morphometry, statistical shape analysis[/su_label] [su_label type="info"]Complex networks theory[/su_label] [su_label type="info"]Graph analysis[/su_label] [su_label type="info"]Longitudinal models[/su_label]

Main applications

[su_label type="info"]Alzheimer’s disease[/su_label] [su_label type="info"]Fronto-temporal dementia[/su_label] [su_label type="info"]Multiple sclerosis[/su_label] [su_label type="info"]Parkinson\'s disease[/su_label] [su_label type="info"]Brain computer interfaces[/su_label]
Main applications :
  • Alzheimer’s disease
  • Fronto-temporal dementia
  • Multiple sclerosis
  • Parkinson\'s disease
  • Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:29:38', '2018-11-16 17:29:38', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1207, 7, '2018-11-16 18:29:51', '2018-11-16 17:29:51', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

[su_label type="info"]Statistical and machine learning[/su_label] [su_label type="info"]Medical image processing[/su_label] [su_label type="info"]Morphometry, statistical shape analysis[/su_label] [su_label type="info"]Complex networks theory[/su_label] [su_label type="info"]Graph analysis[/su_label] [su_label type="info"]Longitudinal models[/su_label]

Main applications

[su_label type="info"]Alzheimer’s disease[/su_label] [su_label type="info"]Fronto-temporal dementia[/su_label] [su_label type="info"]Multiple sclerosis[/su_label] [su_label type="info"]Parkinson\'s disease[/su_label] [su_label type="info"]Brain computer interfaces[/su_label]
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:29:51', '2018-11-16 17:29:51', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1208, 7, '2018-11-16 18:30:39', '2018-11-16 17:30:39', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

[su_label type="info"]Statistical and machine learning[/su_label] [su_label type="info"]Medical image processing[/su_label] [su_label type="info"]Morphometry, statistical shape analysis[/su_label] [su_label type="info"]Complex networks theory[/su_label] [su_label type="info"]Graph analysis[/su_label] [su_label type="info"]Longitudinal models[/su_label]

Main applications

[su_label type="info"]Alzheimer’s disease[/su_label] [su_label type="info"]Fronto-temporal dementia[/su_label] [su_label type="info"]Multiple sclerosis[/su_label] [su_label type="info"]Parkinson\'s disease[/su_label] [su_label type="info"]Brain computer interfaces[/su_label]
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:30:39', '2018-11-16 17:30:39', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1209, 7, '2018-11-16 18:32:20', '2018-11-16 17:32:20', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

[su_label type="info" class="homebuttons"]Statistical and machine learning[/su_label] [su_label type="info" class="homebuttons"]Medical image processing[/su_label] [su_label type="info" class="homebuttons"]Morphometry, statistical shape analysis[/su_label] [su_label type="info" class="homebuttons"]Complex networks theory[/su_label] [su_label type="info"]Graph analysis[/su_label] [su_label type="info" class="homebuttons"]Longitudinal models[/su_label]

Main applications

[su_label type="info"]Alzheimer’s disease[/su_label] [su_label type="info"]Fronto-temporal dementia[/su_label] [su_label type="info"]Multiple sclerosis[/su_label] [su_label type="info"]Parkinson\'s disease[/su_label] [su_label type="info"]Brain computer interfaces[/su_label]
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:32:20', '2018-11-16 17:32:20', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1210, 7, '2018-11-16 18:35:05', '2018-11-16 17:35:05', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

[su_label type="info" class="homebuttons"]Statistical and machine learning[/su_label] [su_label type="info" class="homebuttons"]Medical image processing[/su_label] [su_label type="info" class="homebuttons"]Morphometry, statistical shape analysis[/su_label] [su_label type="info" class="homebuttons"]Complex networks theory[/su_label] [su_label type="info" class="homebuttons"]Graph analysis[/su_label] [su_label type="info" class="homebuttons"]Longitudinal models[/su_label]

Main applications

[su_label type="info"]Alzheimer’s disease[/su_label] [su_label type="info"]Fronto-temporal dementia[/su_label] [su_label type="info"]Multiple sclerosis[/su_label] [su_label type="info"]Parkinson\'s disease[/su_label] [su_label type="info"]Brain computer interfaces[/su_label]
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:35:05', '2018-11-16 17:35:05', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1211, 7, '2018-11-16 18:40:14', '2018-11-16 17:40:14', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

Stastistical and Machine Learning
Medical image processing

Key methododological domains

[su_label type="info" class="homebuttons"]Statistical and machine learning[/su_label] [su_label type="info" class="homebuttons"]Medical image processing[/su_label] [su_label type="info" class="homebuttons"]Morphometry, statistical shape analysis[/su_label] [su_label type="info" class="homebuttons"]Complex networks theory[/su_label] [su_label type="info" class="homebuttons"]Graph analysis[/su_label] [su_label type="info" class="homebuttons"]Longitudinal models[/su_label]

Main applications

[su_label type="info"]Alzheimer’s disease[/su_label] [su_label type="info"]Fronto-temporal dementia[/su_label] [su_label type="info"]Multiple sclerosis[/su_label] [su_label type="info"]Parkinson\'s disease[/su_label] [su_label type="info"]Brain computer interfaces[/su_label]
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:40:14', '2018-11-16 17:40:14', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1212, 7, '2018-11-16 18:40:39', '2018-11-16 17:40:39', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

Stastistical and Machine Learning
Medical image processing

Key methododological domains

[su_label type="info" class="homebuttons"]Statistical and machine learning[/su_label] [su_label type="info" class="homebuttons"]Medical image processing[/su_label] [su_label type="info" class="homebuttons"]Morphometry, statistical shape analysis[/su_label] [su_label type="info" class="homebuttons"]Complex networks theory[/su_label] [su_label type="info" class="homebuttons"]Graph analysis[/su_label] [su_label type="info" class="homebuttons"]Longitudinal models[/su_label]

Main applications

[su_label type="info"]Alzheimer’s disease[/su_label] [su_label type="info"]Fronto-temporal dementia[/su_label] [su_label type="info"]Multiple sclerosis[/su_label] [su_label type="info"]Parkinson\'s disease[/su_label] [su_label type="info"]Brain computer interfaces[/su_label]
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:40:39', '2018-11-16 17:40:39', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1213, 7, '2018-11-16 18:41:14', '2018-11-16 17:41:14', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

Stastistical and Machine Learning
Medical image processing

Key methododological domains

[su_label type="info" class="homebuttons"]Statistical and machine learning[/su_label] [su_label type="info" class="homebuttons"]Medical image processing[/su_label] [su_label type="info" class="homebuttons"]Morphometry, statistical shape analysis[/su_label] [su_label type="info" class="homebuttons"]Complex networks theory[/su_label] [su_label type="info" class="homebuttons"]Graph analysis[/su_label] [su_label type="info" class="homebuttons"]Longitudinal models[/su_label]

Main applications

[su_label type="info"]Alzheimer’s disease[/su_label] [su_label type="info"]Fronto-temporal dementia[/su_label] [su_label type="info"]Multiple sclerosis[/su_label] [su_label type="info"]Parkinson\'s disease[/su_label] [su_label type="info"]Brain computer interfaces[/su_label]
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:41:14', '2018-11-16 17:41:14', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1214, 7, '2018-11-16 18:42:22', '2018-11-16 17:42:22', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

Stastistical and Machine Learning
Medical image processing
Morphometry, statistical shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis

Main applications

[su_label type="info"]Alzheimer’s disease[/su_label] [su_label type="info"]Fronto-temporal dementia[/su_label] [su_label type="info"]Multiple sclerosis[/su_label] [su_label type="info"]Parkinson\'s disease[/su_label] [su_label type="info"]Brain computer interfaces[/su_label]
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:42:22', '2018-11-16 17:42:22', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1215, 7, '2018-11-16 18:43:36', '2018-11-16 17:43:36', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

Stastistical and Machine Learning
Medical image processing
Morphometry, statistical shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis

Main applications

Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:43:36', '2018-11-16 17:43:36', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1216, 7, '2018-11-16 18:44:31', '2018-11-16 17:44:31', '', 'equipe', '', 'inherit', 'closed', 'closed', '', 'equipe', '', '', '2018-11-16 18:44:31', '2018-11-16 17:44:31', '', 1098, 'https://www.aramislab.fr/wp-content/uploads/2018/11/equipe.png', 0, 'attachment', 'image/png', (1217, 7, '2018-11-16 18:44:54', '2018-11-16 17:44:54', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

Stastistical and Machine Learning
Medical image processing
Morphometry, statistical shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis

Main applications

Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials. ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:44:54', '2018-11-16 17:44:54', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1218, 7, '2018-11-16 18:45:18', '2018-11-16 17:45:18', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

Stastistical and Machine Learning
Medical image processing
Morphometry, statistical shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis

Main applications

Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials. ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:45:18', '2018-11-16 17:45:18', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1219, 7, '2018-11-16 18:48:35', '2018-11-16 17:48:35', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

Stastistical and Machine Learning
Medical image processing
Morphometry, statistical shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis

Main applications

Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials. ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:48:35', '2018-11-16 17:48:35', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1220, 7, '2018-11-16 18:54:08', '2018-11-16 17:54:08', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"] AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis

Main applications

Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials. ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 18:54:08', '2018-11-16 17:54:08', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1221, 7, '2018-11-16 18:57:15', '2018-11-16 17:57:15', ' [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 1[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 2[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 3[/su_button]   New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.

  Network theoretic approaches to integrate heterogeneous brain networks

T

 

Spatio-temporal models to build trajectories of disease progression from longitudinal data   Collaborations ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-16 18:57:15', '2018-11-16 17:57:15', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1222, 7, '2018-11-16 20:05:47', '2018-11-16 19:05:47', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]

AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.

ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis

Main applications

Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials. ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 20:05:47', '2018-11-16 19:05:47', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1223, 7, '2018-11-16 20:08:08', '2018-11-16 19:08:08', '[su_slider source="media: 985,982,971,968,966" height="200" mousewheel="no"]
AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis

Main applications

Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-16 20:08:08', '2018-11-16 19:08:08', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1224, 7, '2018-11-16 20:12:00', '2018-11-16 19:12:00', '

Context and general aim

Multiple characteristics of brain diseases can now be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 1[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 2[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 3[/su_button]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.

Network theoretic approaches to integrate heterogeneous brain networks

T

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-16 20:12:00', '2018-11-16 19:12:00', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1225, 7, '2018-11-16 20:41:18', '2018-11-16 19:41:18', '

Faculty

[tmm name="faculty"]

Collaborators

[tmm name="collaborators"]

Post-docs

[tmm name="post-docs"]

Engineers

[tmm name="engineers"]

PhD Students

[tmm name="phd-students"]

Master students

[tmm name="master-students"]

Postdocs

  • Takoua Kaaouana - takoua.kaaouana@upmc.fr
  • Xavier Navarro - x.navarro.s@gmail.com

Former Members

  • Michael Bacci
  • Sabrina Fontanella
  • Clementine Fourrier
  • Federico Battiston
  • Takoua Kaaouana
  • Xavier Navarro
  • Camille Chrétien
  • Hugo Boniface
  • Jungying Fang
  • Romain Valabrègue - Inserm Research Engineer - webpage - romain.valabregue@upmc.fr
  • Eric Bardinet - CNRS Research Engineer - webpage - eric.bardinet@upmc.fr
  • Sara Fernandez-Vidal - Inserm Research Engineer - webpage - sara.fernandez_vidal@upmc.fr
  • Vera Dinkelacker - Rothschild Foundation - v.dinkelacker@gmail.com
  • Thomas Estienne - UPMC, CATI project - thomas.estienne@icm-institute.org
  • Martina Sundqvist - Ecole Normale Supérieure de Paris
  • Thomas Jacquemont - Ecole Normale Supérieure de Paris
  • Aurore Bussalb - Institut Supérieur de BioSciences de Paris
  • Carlos Tor Diez - Télécom ParisTech
  • Ayoub Louati - Ecole Polytechnique de Tunisie
  • Paul Jusselin - Ecole Normale Supérieure de Cachan
  • Chabha Azouani - CATI project - chabha.azouani@upmc.fr
  • Kelly Martineau - CATI project – kelly.martineau@icm-institute.org
  • Sonia Djobeir - CATI project – soniadjobeir@gmail.com
  • Hugo Dary - CATI project - dary.hugo@gmail.com
  • Ludovic Fillon - CATI project - ludovic.fillon@upmc.fr
  • Mathieu Dubois - CATI project - mathieu.dubois@icm-institute.org
  • Barbara Gris - gris@cmla.ens-cachan.fr
  • Géraldine Rousseau - geraldine.rouseau@psl.ap-hop-paris.fr
  • Marika Rudler - marika.rudler@psl.aphp.fr
  • Jean-Baptiste Schiratti - jean-baptiste.schiratti@cmap.polytechnique.fr
  • Pietro Gori - Postdoctoral fellow - Now Assistant Professor, Telecom ParisTech
  • Ana Fouquier - - Postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Alexandre Pron - Master\'s student (Université Paris Descartes) - Now PhD student at INT, Marseille
  • Susovan Pal - Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Engineer - Now Pipeline TD Junior at Ellipse Studio
  • François Deloche - Intern (École Polytechnique)
  • Alexis Mocellin - Intern (Ecole Polytechnique)
  • Claire Cury - PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Clinical Research Associate - Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Clinical Research Associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Kanza Dekkiche - Master\'s student
  • Antoine Latrille - Master\'s student (ESIEE Paris) - Now Ingénieur Développeur Web at Easyvoyage
  • Guillaume Ruffin - Intern
  • Evgeny Zuenko - Master\'s student
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Maxime Corduant - Master\'s student
  • Kevin Roussel - Master\'s student
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP - claude.adam@psl.aphp.fr
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP - sophie.dupont@psl.aphp.fr
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP - damien.galanaud@icm-institute.org
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP - yves.samson@psl.aphp.fr
  • Lionel Thivard - Neurologist (PH), AP-HP - lionel.thivard@psl.aphp.fr
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Faculty

[tmm name="faculty"]

Collaborators

[tmm name="collaborators"]

Post-docs

[tmm name="post-docs"]

Engineers

[tmm name="engineers"]

PhD Students

[tmm name="phd-students"]

Master students

[tmm name="master-students"]

Former Members

  • Michael Bacci
  • Sabrina Fontanella
  • Clementine Fourrier
  • Federico Battiston
  • Takoua Kaaouana
  • Xavier Navarro
  • Camille Chrétien
  • Hugo Boniface
  • Jungying Fang
  • Romain Valabrègue - Inserm Research Engineer - webpage - romain.valabregue@upmc.fr
  • Eric Bardinet - CNRS Research Engineer - webpage - eric.bardinet@upmc.fr
  • Sara Fernandez-Vidal - Inserm Research Engineer - webpage - sara.fernandez_vidal@upmc.fr
  • Vera Dinkelacker - Rothschild Foundation - v.dinkelacker@gmail.com
  • Thomas Estienne - UPMC, CATI project - thomas.estienne@icm-institute.org
  • Martina Sundqvist - Ecole Normale Supérieure de Paris
  • Thomas Jacquemont - Ecole Normale Supérieure de Paris
  • Aurore Bussalb - Institut Supérieur de BioSciences de Paris
  • Carlos Tor Diez - Télécom ParisTech
  • Ayoub Louati - Ecole Polytechnique de Tunisie
  • Paul Jusselin - Ecole Normale Supérieure de Cachan
  • Chabha Azouani - CATI project - chabha.azouani@upmc.fr
  • Kelly Martineau - CATI project – kelly.martineau@icm-institute.org
  • Sonia Djobeir - CATI project – soniadjobeir@gmail.com
  • Hugo Dary - CATI project - dary.hugo@gmail.com
  • Ludovic Fillon - CATI project - ludovic.fillon@upmc.fr
  • Mathieu Dubois - CATI project - mathieu.dubois@icm-institute.org
  • Barbara Gris - gris@cmla.ens-cachan.fr
  • Géraldine Rousseau - geraldine.rouseau@psl.ap-hop-paris.fr
  • Marika Rudler - marika.rudler@psl.aphp.fr
  • Jean-Baptiste Schiratti - jean-baptiste.schiratti@cmap.polytechnique.fr
  • Pietro Gori - Postdoctoral fellow - Now Assistant Professor, Telecom ParisTech
  • Ana Fouquier - - Postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Alexandre Pron - Master\'s student (Université Paris Descartes) - Now PhD student at INT, Marseille
  • Susovan Pal - Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Engineer - Now Pipeline TD Junior at Ellipse Studio
  • François Deloche - Intern (École Polytechnique)
  • Alexis Mocellin - Intern (Ecole Polytechnique)
  • Claire Cury - PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Clinical Research Associate - Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Clinical Research Associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Kanza Dekkiche - Master\'s student
  • Antoine Latrille - Master\'s student (ESIEE Paris) - Now Ingénieur Développeur Web at Easyvoyage
  • Guillaume Ruffin - Intern
  • Evgeny Zuenko - Master\'s student
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Maxime Corduant - Master\'s student
  • Kevin Roussel - Master\'s student
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP - claude.adam@psl.aphp.fr
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP - sophie.dupont@psl.aphp.fr
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP - damien.galanaud@icm-institute.org
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP - yves.samson@psl.aphp.fr
  • Lionel Thivard - Neurologist (PH), AP-HP - lionel.thivard@psl.aphp.fr
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AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis

Main applications

Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
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AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis

Main applications

Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
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AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis

Main applications

Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
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AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis

Main applications

Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
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AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis

Main applications

Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
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AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis

Main applications

Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
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AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis

Main applications

Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
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AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

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Lorem Ipsum

Key methododological domains

Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis

Main applications

Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
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AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

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Lorem Ipsum

Key methododological domains

Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis

Main applications

Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
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AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

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Lorem Ipsum

Key methododological domains

Stastistical and Machine Learning
Medical image processing
Morphometry and shape analysis
Complex networks theory
Graph analysis
Longitudinal analysis

Main applications

Alzheimer\'s disease
Fronto-temporal dementia
Multiple sclerosis
Parkinson\'s disease
Brain computer interfaces
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
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AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

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Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy"]Hidden content[/su_spoiler] [/su_accordion]
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
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AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy"]Hidden content[/su_spoiler] [/su_accordion]
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
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AramisLab is a joint research team between CNRS, Inria, Inserm and UPMC (University Pierre et Marie Curie) within the Brain and Spine Institute (ICM). The ICM is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, which is the largest adult hospital in Europe and has a long tradition of neuroscience and neurology.
ARAMIS is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging). The team has close and enduring collaborations with different clinical teams of the ICM and the Pitié-Salpêtrière hospital.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]
The general objective of the team is to build numerical models of brain diseases from multimodal patient data (medical imaging, clinical data, genomic data). To achieve this goal, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
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Context and general aim

Multiple characteristics of brain diseases can now be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 1[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 2[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_divider style="default" divider_color="#702082" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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Context and general aim

Multiple characteristics of brain diseases can now be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 1[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 2[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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Context and general aim

Multiple characteristics of brain diseases can now be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 1[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 2[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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Context and general aim

Multiple characteristics of brain diseases can now be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 1[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 2[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]
', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-18 11:50:31', '2018-11-18 10:50:31', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1254, 7, '2018-11-18 11:51:30', '2018-11-18 10:51:30', '[su_slider source="media:982,971,968,966" height="200" mousewheel="no"] [su_quote cite="John Doe" url="#"]As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.[/su_quote]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]
  ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-18 12:10:42', '2018-11-18 11:10:42', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1260, 7, '2018-11-18 12:11:12', '2018-11-18 11:11:12', '[su_slider source="media:982,971,968,966" height="200" mousewheel="no"]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]
  ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-18 12:11:12', '2018-11-18 11:11:12', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1261, 7, '2018-11-18 12:12:03', '2018-11-18 11:12:03', '[su_slider source="media:982,971,968,966" height="200" mousewheel="no"]
As a joint research team {CNRS, Inria, Inserm, UPMC }, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]
  ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-18 12:12:03', '2018-11-18 11:12:03', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1262, 7, '2018-11-18 12:13:03', '2018-11-18 11:13:03', '[su_slider source="media:982,971,968,966" height="200" mousewheel="no"]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]
  ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-18 12:13:03', '2018-11-18 11:13:03', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1263, 7, '2018-11-18 12:13:56', '2018-11-18 11:13:56', '[su_slider source="media:982,971,968,966" height="200" mousewheel="no"]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron"]Hidden content[/su_spoiler] [/su_accordion]
  ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-18 12:13:56', '2018-11-18 11:13:56', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1264, 7, '2018-11-18 12:17:24', '2018-11-18 11:17:24', '[su_slider source="media:982,971,968,966" height="200" mousewheel="no"]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
  ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-18 12:17:24', '2018-11-18 11:17:24', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1265, 7, '2018-11-18 23:01:39', '2018-11-18 22:01:39', '', 'map', '', 'inherit', 'closed', 'closed', '', 'map', '', '', '2018-11-18 23:01:39', '2018-11-18 22:01:39', '', 0, 'https://www.aramislab.fr/wp-content/uploads/2018/11/map.png', 0, 'attachment', 'image/png', (1266, 7, '2018-11-18 23:17:04', '2018-11-18 22:17:04', '[su_slider source="media:982,971,968,966" height="200" mousewheel="no" responsive="yes" title="no"]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
  ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-18 23:17:04', '2018-11-18 22:17:04', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1267, 7, '2018-11-18 23:23:04', '2018-11-18 22:23:04', '[su_slider source="media: 968,966,958" width="380" height="260"]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

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Main applications

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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

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Main applications

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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

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  [caption id="attachment_1227" align="center" width="4160"]Team retreat, Villa Finaly, Florence, Italy[/caption]', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-18 23:29:20', '2018-11-18 22:29:20', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1272, 7, '2018-11-18 23:30:12', '2018-11-18 22:30:12', '[su_slider source="media: 968,966,1015,958,971" width="1600" height="600" title="no" mousewheel="no"]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

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Main applications

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  [caption id="attachment_1227" align="center" width="4160"]
Team retreat, Villa Finaly, Florence, Italy
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

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Main applications

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  [caption id="attachment_1227" align="center" width="4160"]
Team retreat at the Villa Finaly, Florence, Italy
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

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Main applications

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  [caption id="attachment_1227" align="center" width="4160"]
Team retreat at the Villa Finaly, Florence, Italy
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

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Main applications

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  [caption id="attachment_1227" align="center" width="auto"]
Team retreat at the Villa Finaly, Florence, Italy
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

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  [caption id="attachment_1227" align="center" width="100%"]
Team retreat at the Villa Finaly, Florence, Italy
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
  [caption id="attachment_1227" align="center"]
Team retreat at the Villa Finaly, Florence, Italy
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' I

Postdocs / Scientists

PhD thesis

Engineers / Software developers

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If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).

Postdocs / Scientists

PhD thesis

Engineers / Software developers

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If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the permanent researchers of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).

Postdocs / Scientists

PhD thesis

Engineers / Software developers

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Most representative publications

Networks

  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017.PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM journal on Numerical Analysis 2017. 56(4), 256-2584PDF Paper in PDF
  • Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; ADNI; AIBL. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. In Neuroimage 2018. PDF Paper in PDF
  • Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 357-364. 2017. PDF Paper in PDF
  • Wei, W., Poirion, E., Bodini, B., Durrleman, S., Ayache, N., Stankoff, B., Colliot, O. Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training. In MICCAI. 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinical studies

  • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
  • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
  • Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. PDF Paper in PDF
  • Dubois B, Epelbaum S, Nyasse F, Bakardjian H, Gagliardi G, Uspenskaya O, Houot M, Lista S, Cacciamani F, Potier MC, Bertrand A, Lamari F, Benali H, Mangin JF, Colliot O, Genthon R, Habert MO, Hampel H; INSIGHT-preAD study group. Cognitive and neuroimaging features and brain β-amyloidosis in individuals at risk of Alzheimer\'s disease (INSIGHT-preAD): a longitudinal observational study.. In Lancet Neurol.. 2018, Apr;17(4):335-346. PDF Paper in PDF
  • Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-autosave-v1', '', '', '2018-12-05 17:43:21', '2018-12-05 16:43:21', '', 26, 'https://www.aramislab.fr/26-autosave-v1/', 0, 'revision', '', (1286, 7, '2018-11-19 00:07:26', '2018-11-18 23:07:26', '

Most representative publications

Clinica: an open source software platform for reproducible clinical neuroscience studies
·
Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 00:07:26', '2018-11-18 23:07:26', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1287, 7, '2018-11-19 00:08:03', '2018-11-18 23:08:03', '

Most representative publications

Clinica: an open source software platform for reproducible clinical neuroscience studies ·Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 00:08:03', '2018-11-18 23:08:03', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1288, 7, '2018-11-19 00:08:37', '2018-11-18 23:08:37', '

Most representative publications

Clinica: an open source software platform for reproducible clinical neuroscience studies ·Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 00:08:37', '2018-11-18 23:08:37', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1289, 7, '2018-11-19 00:08:56', '2018-11-18 23:08:56', '

Most representative publications

Clinica: an open source software platform for reproducible clinical neuroscience studies ·Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 00:08:56', '2018-11-18 23:08:56', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1290, 7, '2018-11-19 00:09:06', '2018-11-18 23:09:06', '

Most representative publications

Clinica: an open source software platform for reproducible clinical neuroscience studies Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 00:09:06', '2018-11-18 23:09:06', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1291, 7, '2018-11-19 00:09:25', '2018-11-18 23:09:25', '

Most representative publications

Clinica: an open source software platform for reproducible clinical neuroscience studies

Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.

      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 00:09:25', '2018-11-18 23:09:25', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1292, 7, '2018-11-19 00:09:42', '2018-11-18 23:09:42', '

Most representative publications

Clinica: an open source software platform for reproducible clinical neuroscience studies Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 00:09:42', '2018-11-18 23:09:42', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1293, 7, '2018-11-19 00:17:35', '2018-11-18 23:17:35', '

Context and general aim

Multiple characteristics of brain diseases can now be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 1[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 2[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 1[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 2[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 00:19:08', '2018-11-18 23:19:08', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1295, 7, '2018-11-19 00:23:13', '2018-11-18 23:23:13', '

Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="ghost" background="#00b512" color="#ffffff"]Publication 1[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 2[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="flat" background="#00b512" color="#ffffff"]Publication 1[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 2[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 00:23:31', '2018-11-18 23:23:31', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1297, 7, '2018-11-19 00:23:49', '2018-11-18 23:23:49', '

Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="soft" background="#00b512" color="#ffffff"]Publication 1[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 2[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 00:23:49', '2018-11-18 23:23:49', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1298, 7, '2018-11-19 00:24:35', '2018-11-18 23:24:35', '

Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="soft" background="#00b512" color="#ffffff"]Publication 1[/su_button] [su_button url="#" size="6" style="flat" background="#00b512" color="#ffffff"]Publication 2[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="glass" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="bubbles" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="noise" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="strocked" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="3D" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 00:24:35', '2018-11-18 23:24:35', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1299, 7, '2018-11-19 10:02:09', '2018-11-19 09:02:09', '

Most representative publications

Clinica: an open source software platform for reproducible clinical neuroscience studies Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
okok2
      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 10:02:09', '2018-11-19 09:02:09', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1300, 7, '2018-11-19 10:02:20', '2018-11-19 09:02:20', '

Most representative publications

Clinica: an open source software platform for reproducible clinical neuroscience studies Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
okok2
      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 10:02:20', '2018-11-19 09:02:20', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1301, 7, '2018-11-19 10:02:30', '2018-11-19 09:02:30', '

Most representative publications

Clinica: an open source software platform for reproducible clinical neuroscience studiesRoutier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
okok2
      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 10:02:30', '2018-11-19 09:02:30', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1302, 7, '2018-11-19 10:09:54', '2018-11-19 09:09:54', '

Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="soft" background="#00b512" color="#ffffff"]Publication 1[/su_button] [su_button url="#" size="6" style="flat" background="#00b512" color="#ffffff"]Publication 2[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="glass" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="bubbles" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="noise" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="strocked" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="3D" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 10:09:54', '2018-11-19 09:09:54', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1303, 7, '2018-11-19 10:10:05', '2018-11-19 09:10:05', '

Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="soft" background="#00b512" color="#ffffff"]Publication 1[/su_button] [su_button url="#" size="6" style="flat" background="#00b512" color="#ffffff"]Publication 2[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="glass" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="bubbles" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="noise" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="strocked" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="3D" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 10:10:05', '2018-11-19 09:10:05', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1304, 7, '2018-11-19 10:10:35', '2018-11-19 09:10:35', '

Faculty

[tmm name="faculty"]

Collaborators

[tmm name="collaborators"]

Post-docs

[tmm name="post-docs"]

Engineers

[tmm name="engineers"]

PhD Students

[tmm name="phd-students"]

Master students

[tmm name="master-students"]

Former Members

  • Michael Bacci
  • Sabrina Fontanella
  • Clementine Fourrier
  • Federico Battiston
  • Takoua Kaaouana
  • Xavier Navarro
  • Camille Chrétien
  • Hugo Boniface
  • Jungying Fang
  • Romain Valabrègue - Inserm Research Engineer - webpage - romain.valabregue@upmc.fr
  • Eric Bardinet - CNRS Research Engineer - webpage - eric.bardinet@upmc.fr
  • Sara Fernandez-Vidal - Inserm Research Engineer - webpage - sara.fernandez_vidal@upmc.fr
  • Vera Dinkelacker - Rothschild Foundation - v.dinkelacker@gmail.com
  • Thomas Estienne - UPMC, CATI project - thomas.estienne@icm-institute.org
  • Martina Sundqvist - Ecole Normale Supérieure de Paris
  • Thomas Jacquemont - Ecole Normale Supérieure de Paris
  • Aurore Bussalb - Institut Supérieur de BioSciences de Paris
  • Carlos Tor Diez - Télécom ParisTech
  • Ayoub Louati - Ecole Polytechnique de Tunisie
  • Paul Jusselin - Ecole Normale Supérieure de Cachan
  • Chabha Azouani - CATI project - chabha.azouani@upmc.fr
  • Kelly Martineau - CATI project – kelly.martineau@icm-institute.org
  • Sonia Djobeir - CATI project – soniadjobeir@gmail.com
  • Hugo Dary - CATI project - dary.hugo@gmail.com
  • Ludovic Fillon - CATI project - ludovic.fillon@upmc.fr
  • Mathieu Dubois - CATI project - mathieu.dubois@icm-institute.org
  • Barbara Gris - gris@cmla.ens-cachan.fr
  • Géraldine Rousseau - geraldine.rouseau@psl.ap-hop-paris.fr
  • Marika Rudler - marika.rudler@psl.aphp.fr
  • Jean-Baptiste Schiratti - jean-baptiste.schiratti@cmap.polytechnique.fr
  • Pietro Gori - Postdoctoral fellow - Now Assistant Professor, Telecom ParisTech
  • Ana Fouquier - - Postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Alexandre Pron - Master\'s student (Université Paris Descartes) - Now PhD student at INT, Marseille
  • Susovan Pal - Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Engineer - Now Pipeline TD Junior at Ellipse Studio
  • François Deloche - Intern (École Polytechnique)
  • Alexis Mocellin - Intern (Ecole Polytechnique)
  • Claire Cury - PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Clinical Research Associate - Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Clinical Research Associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Kanza Dekkiche - Master\'s student
  • Antoine Latrille - Master\'s student (ESIEE Paris) - Now Ingénieur Développeur Web at Easyvoyage
  • Guillaume Ruffin - Intern
  • Evgeny Zuenko - Master\'s student
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Maxime Corduant - Master\'s student
  • Kevin Roussel - Master\'s student
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP - claude.adam@psl.aphp.fr
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP - sophie.dupont@psl.aphp.fr
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP - damien.galanaud@icm-institute.org
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP - yves.samson@psl.aphp.fr
  • Lionel Thivard - Neurologist (PH), AP-HP - lionel.thivard@psl.aphp.fr
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2018-11-19 10:10:35', '2018-11-19 09:10:35', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '', (1305, 7, '2018-11-19 10:11:10', '2018-11-19 09:11:10', '

Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="soft" background="#00b512" color="#ffffff"]Publication 1[/su_button] [su_button url="#" size="6" style="flat" background="#00b512" color="#ffffff"]Publication 2[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="glass" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="bubbles" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="noise" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="strocked" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_button url="#" size="6" style="default" background="#00b512" color="#ffffff"]Publication 3[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 10:11:10', '2018-11-19 09:11:10', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1306, 7, '2018-11-19 10:12:48', '2018-11-19 09:12:48', '

Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="soft" background="#702082" color="#ffffff"]Publication 1[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 10:12:48', '2018-11-19 09:12:48', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1307, 7, '2018-11-19 10:13:06', '2018-11-19 09:13:06', '

Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="flat" background="#702082" color="#ffffff"]Publication 1[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 10:13:06', '2018-11-19 09:13:06', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1308, 7, '2018-11-19 10:14:26', '2018-11-19 09:14:26', '

Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="ghost" background="#702082" color="#ffffff"]Publication 1[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 10:14:26', '2018-11-19 09:14:26', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1309, 7, '2018-11-19 10:16:13', '2018-11-19 09:16:13', '

Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="ghost" background="#702082" color="#ffffff" center="yes"]Publication 1[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 10:16:13', '2018-11-19 09:16:13', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1310, 7, '2018-11-19 10:16:34', '2018-11-19 09:16:34', '

Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="ghost" background="#702082" color="#702082" center="yes"]Publication 1[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 10:16:34', '2018-11-19 09:16:34', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1311, 7, '2018-11-19 10:17:47', '2018-11-19 09:17:47', '

Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="ghost" background="#702082" color="#702082" center="yes"]Publication 1[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_button url="#" size="6" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 10:17:47', '2018-11-19 09:17:47', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1312, 7, '2018-11-19 10:18:09', '2018-11-19 09:18:09', '

Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="ghost" background="#702082" color="#702082" center="yes"]Publication 1[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 10:18:09', '2018-11-19 09:18:09', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1313, 7, '2018-11-19 10:18:35', '2018-11-19 09:18:35', '

Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="ghost" background="#702082" color="#702082" center="yes"]Publication 1[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 10:18:35', '2018-11-19 09:18:35', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1314, 7, '2018-11-19 10:18:56', '2018-11-19 09:18:56', '

Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="ghost" background="#702082" color="#702082" center="yes"]Publication 1[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 10:18:56', '2018-11-19 09:18:56', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1315, 7, '2018-11-19 10:19:19', '2018-11-19 09:19:19', '

Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="6" style="ghost" background="#702082" color="#702082" center="yes"]Publication 1[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 10:19:19', '2018-11-19 09:19:19', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1316, 7, '2018-11-19 10:21:52', '2018-11-19 09:21:52', '

Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]

Learning spatiotemporal trajectories from manifold-valued longitudinal data.

Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]

Learning spatiotemporal trajectories from manifold-valued longitudinal data.

Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Non-parametric resampling of random walks for spectral networks clustering. De Vico Fallani et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...). In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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Context and general aim

Multiple characteristics of brain diseases can be measured in living patients thanks to the tremendous progress of neuroimaging, genomic and biomarker technologies. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms.The general aim of the team is to build numerical models of brain diseases from multimodal patient databased on the development of specific data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient including neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics...).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="Tab name" disabled="no" anchor="" url="" target="blank" class=""]Tab content[/su_tab] [su_tab title="Tab name" disabled="no" anchor="" url="" target="blank" class=""]Tab content[/su_tab] [su_tab title="Tab name" disabled="no" anchor="" url="" target="blank" class=""]Tab content[/su_tab] [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 10:32:01', '2018-11-19 09:32:01', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1326, 7, '2018-11-19 10:33:12', '2018-11-19 09:33:12', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London" disabled="no" anchor="" url="" target="blank" class=""]Center for Medical Image Computing (Sébastien Ourselin, Daniel Alexander)[/su_tab] [su_tab title="Tab name" disabled="no" anchor="" url="" target="blank" class=""]Tab content[/su_tab] [su_tab title="Tab name" disabled="no" anchor="" url="" target="blank" class=""]Tab content[/su_tab] [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

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Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London" disabled="no" anchor="" url="" target="blank" class=""]Center for Medical Image Computing Sébastien Ourselin, Daniel Alexander[/su_tab] [su_tab title="Tab name" disabled="no" anchor="" url="" target="blank" class=""]Tab content[/su_tab] [su_tab title="Tab name" disabled="no" anchor="" url="" target="blank" class=""]Tab content[/su_tab] [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 10:33:31', '2018-11-19 09:33:31', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1328, 7, '2018-11-19 10:34:34', '2018-11-19 09:34:34', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London" disabled="no" anchor="" url="" target="blank" class=""]Center for Medical Image Computing Sébastien Ourselin, Daniel Alexander[/su_tab] [su_tab title="Scientific Computing and Imaging (SCI) Institute" disabled="no" anchor="" url="" target="blank" class=""]Tab content[/su_tab] [su_tab title="Tab name" disabled="no" anchor="" url="" target="blank" class=""]Tab content[/su_tab] [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 10:34:34', '2018-11-19 09:34:34', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1329, 7, '2018-11-19 10:36:06', '2018-11-19 09:36:06', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London" disabled="no" anchor="" url="" target="blank" class=""]Center for Medical Image Computing Sébastien Ourselin, Daniel Alexander[/su_tab] [su_tab title="Scientific Computing and Imaging (SCI) Institute" disabled="no" anchor="" url="" target="blank" class=""]Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab] [su_tab title="Departement of Physics. Queen Mary University of London" disabled="no" anchor="" url="" target="blank" class=""]Vito Latora[/su_tab] [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 10:36:06', '2018-11-19 09:36:06', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1330, 7, '2018-11-19 10:37:09', '2018-11-19 09:37:09', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London" disabled="no" anchor="" url="" target="blank" class=""]Center for Medical Image Computing Sébastien Ourselin, Daniel Alexander[/su_tab] [su_tab title="Scientific Computing and Imaging (SCI) Institute" disabled="no" anchor="" url="" target="blank" class=""]Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab] [su_tab title="Departement of Physics. Queen Mary University of London" disabled="no" anchor="" url="" target="blank" class=""]Vito Latora[/su_tab] [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

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Brain network toolbox

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:39:33', '2018-11-19 09:39:33', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1332, 7, '2018-11-19 10:42:00', '2018-11-19 09:42:00', '

Brain network toolbox

Logo_alto_100
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:42:00', '2018-11-19 09:42:00', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1333, 7, '2018-11-19 10:42:40', '2018-11-19 09:42:40', '

Brain network toolbox

Logo_alto_100
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:42:40', '2018-11-19 09:42:40', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1334, 7, '2018-11-19 10:43:51', '2018-11-19 09:43:51', '
Logo_alto_100

Brain network toolbox

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:43:51', '2018-11-19 09:43:51', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1335, 7, '2018-11-19 10:49:31', '2018-11-19 09:49:31', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:49:31', '2018-11-19 09:49:31', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1336, 7, '2018-11-19 10:49:55', '2018-11-19 09:49:55', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:49:55', '2018-11-19 09:49:55', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1337, 7, '2018-11-19 10:50:33', '2018-11-19 09:50:33', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Website[/su_button][su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Contact[/su_button] [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:50:33', '2018-11-19 09:50:33', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1338, 7, '2018-11-19 10:50:55', '2018-11-19 09:50:55', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="no"]Website[/su_button] [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="no"]Contact[/su_button] [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:50:55', '2018-11-19 09:50:55', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1339, 7, '2018-11-19 10:51:16', '2018-11-19 09:51:16', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Website[/su_button] [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="no"]Contact[/su_button] [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:51:16', '2018-11-19 09:51:16', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1340, 7, '2018-11-19 10:52:15', '2018-11-19 09:52:15', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Website[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:52:15', '2018-11-19 09:52:15', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1341, 7, '2018-11-19 10:52:32', '2018-11-19 09:52:32', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="5" style="ghost" background="#702082" color="#702082" center="yes"]Website[/su_button]
[su_button url="#" size="5" style="ghost" background="#702082" color="#702082" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:52:32', '2018-11-19 09:52:32', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1342, 7, '2018-11-19 10:53:36', '2018-11-19 09:53:36', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="5" style="ghost" background="#702082" color="#702082" center="yes"]Website[/su_button]
[su_button url="#" size="5" style="ghost" background="#702082" color="#702082" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:53:36', '2018-11-19 09:53:36', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1343, 7, '2018-11-19 10:53:59', '2018-11-19 09:53:59', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="5" style="ghost" background="#702082" color="#702082" center="yes"]Website[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:53:59', '2018-11-19 09:53:59', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1344, 7, '2018-11-19 10:54:45', '2018-11-19 09:54:45', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="5" style="ghost" background="#702082" color="#702082" center="yes"]Website[/su_button]
[su_button url="mailto:person1@domain.com,person2@domain.com" size="5" style="ghost" background="#702082" color="#702082" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:54:45', '2018-11-19 09:54:45', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1345, 7, '2018-11-19 10:55:41', '2018-11-19 09:55:41', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="5" style="ghost" background="#702082" color="#702082" center="yes"]Website[/su_button]
[su_button url="mailto:person1@domain.com,person2@domain.com" size="5" style="flat" background="#702082" color="#702082" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:55:41', '2018-11-19 09:55:41', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1346, 7, '2018-11-19 10:56:01', '2018-11-19 09:56:01', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="5" style="ghost" background="#702082" color="#702082" center="yes"]Website[/su_button]
[su_button url="mailto:person1@domain.com,person2@domain.com" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:56:01', '2018-11-19 09:56:01', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1347, 7, '2018-11-19 10:56:29', '2018-11-19 09:56:29', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="5" style="ghost" background="#702082" color="#702082" center="yes"]Website[/su_button]
[su_button url="mailto:person1@domain.com,person2@domain.com" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:56:29', '2018-11-19 09:56:29', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1348, 7, '2018-11-19 10:57:52', '2018-11-19 09:57:52', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani[at]gmail[dot]com,mario.chavez[at]upmc[dot]fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:57:52', '2018-11-19 09:57:52', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1349, 7, '2018-11-19 10:58:22', '2018-11-19 09:58:22', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani@gmail.com,mario.chavez(at)upmc(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:58:22', '2018-11-19 09:58:22', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1350, 7, '2018-11-19 10:58:36', '2018-11-19 09:58:36', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Logo_alto_100 A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.

Contacts: fabrizio.devicofallani@gmail.com and mario.chavez@upmc.fr Reference: https://sites.google.com/site/fr2eborn/download [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 10:58:36', '2018-11-19 09:58:36', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1351, 7, '2018-11-19 11:00:28', '2018-11-19 10:00:28', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="http://www.clinica.run/" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 11:00:28', '2018-11-19 10:00:28', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1352, 7, '2018-11-19 11:01:59', '2018-11-19 10:01:59', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button] [su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="http://www.clinica.run/" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 11:01:59', '2018-11-19 10:01:59', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1353, 7, '2018-11-19 11:02:12', '2018-11-19 10:02:12', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button] [su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="http://www.clinica.run/" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 11:02:12', '2018-11-19 10:02:12', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1354, 7, '2018-11-19 11:02:31', '2018-11-19 10:02:31', '

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button] [su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="http://www.clinica.run/" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 11:02:31', '2018-11-19 10:02:31', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1355, 7, '2018-11-19 11:04:57', '2018-11-19 10:04:57', '

Brain network toolbox

Logo_alto_100 [su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button] [su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="http://www.clinica.run/" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 11:04:57', '2018-11-19 10:04:57', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1356, 7, '2018-11-19 11:05:22', '2018-11-19 10:05:22', '
Logo_alto_100 [su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button] [su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]

Brain network toolbox

A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="http://www.clinica.run/" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 11:05:22', '2018-11-19 10:05:22', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1357, 7, '2018-11-19 11:09:57', '2018-11-19 10:09:57', '
Logo_alto_100 [su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button] [su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]

Brain network toolbox

A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="http://www.clinica.run/" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 11:09:57', '2018-11-19 10:09:57', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1358, 7, '2018-11-19 11:11:41', '2018-11-19 10:11:41', '
Logo_alto_100 [su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button] [su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]

Brain network toolbox

A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="http://www.clinica.run/" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button] [su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 11:11:41', '2018-11-19 10:11:41', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1359, 7, '2018-11-19 11:12:43', '2018-11-19 10:12:43', '
Logo_alto_100 [su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button] [su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]

Brain network toolbox

A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="http://www.clinica.run/" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button] [su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 11:12:43', '2018-11-19 10:12:43', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1360, 7, '2018-11-19 11:14:06', '2018-11-19 10:14:06', '
Logo_alto_100 [su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button] [su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]

Brain network toolbox

A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="http://www.clinica.run/" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button] [su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

ClinicaClinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG. For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.

Contacts: olivier.colliot@inria.fr Some papers using Clinica:
  1. T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging, Elsevier, 2017.
  2. A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol, 2017.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 11:14:06', '2018-11-19 10:14:06', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1361, 7, '2018-11-19 11:14:34', '2018-11-19 10:14:34', '
Logo_alto_100 [su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button] [su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]

Brain network toolbox

A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="http://www.clinica.run/" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button] [su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 11:14:34', '2018-11-19 10:14:34', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1362, 7, '2018-11-19 11:16:19', '2018-11-19 10:16:19', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button] [su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica_logo_altoDeformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Contacts: stanley.durrleman@inria.fr Reference: S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014. [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Logo_alto_100 [su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button] [su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]

Brain network toolbox

A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"] ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 11:16:19', '2018-11-19 10:16:19', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1363, 7, '2018-11-19 11:19:04', '2018-11-19 10:19:04', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button] [su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="http://www.sciencedirect.com/science/article/pii/S1053811914005205" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
Logo_alto_100 [su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="default" background="#702082" color="white" center="yes"]Website[/su_button] [su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="default" background="#702082" color="white" center="yes"]Contact[/su_button]

Brain network toolbox

A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory [su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Reference to complete[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"] ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 11:19:04', '2018-11-19 10:19:04', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1364, 7, '2018-11-19 11:21:09', '2018-11-19 10:21:09', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button] [su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="http://www.sciencedirect.com/science/article/pii/S1053811914005205" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 11:21:09', '2018-11-19 10:21:09', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1365, 7, '2018-11-19 11:22:31', '2018-11-19 10:22:31', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button] [su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="http://www.sciencedirect.com/science/article/pii/S1053811914005205" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 11:22:31', '2018-11-19 10:22:31', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1366, 7, '2018-11-19 11:25:01', '2018-11-19 10:25:01', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London" disabled="no" anchor="" url="" target="blank" class=""] Center for Medical Image Computing Sébastien Ourselin, Daniel Alexander[/su_tab] [su_tab title="Scientific Computing and Imaging (SCI) Institute" disabled="no" anchor="" url="" target="blank" class=""]Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab] [su_tab title="Departement of Physics. Queen Mary University of London" disabled="no" anchor="" url="" target="blank" class=""]Vito Latora[/su_tab] [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

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Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""] Center for Medical Image Computing Description to complete Sébastien Ourselin, Daniel Alexander[/su_tab] [su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""] Scientific Computing and Imaging (SCI) Institute, University of Utah, USA Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab] [su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""] Departement of Physics. Queen Mary University of London, UK Vito Latora[/su_tab] [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 11:27:15', '2018-11-19 10:27:15', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1368, 7, '2018-11-19 11:27:38', '2018-11-19 10:27:38', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""] Center for Medical Image Computing Description to complete Sébastien Ourselin, Daniel Alexander[/su_tab] [su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""] Scientific Computing and Imaging (SCI) Institute, University of Utah, USA Description to complete Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab] [su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""] Departement of Physics. Queen Mary University of London, UK Description to complete Vito Latora[/su_tab] [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 11:27:38', '2018-11-19 10:27:38', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1369, 7, '2018-11-19 11:31:30', '2018-11-19 10:31:30', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""] Center for Medical Image Computing Description to complete Contacts: Sébastien Ourselin, Daniel Alexander[/su_tab] [su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""] Scientific Computing and Imaging (SCI) Institute, University of Utah, USA Description to complete Contacts: Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab] [su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""] Departement of Physics. Queen Mary University of London, UK Description to complete Contacts: Vito Latora[/su_tab] [/su_tabs] [su_tab title="University of Minnesota" disabled="no" anchor="" url="" target="blank" class=""] Center for Magnetic Resonance Research, University of Minnesota, USA Description to complete Contacts : Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil [/su_tabs] [su_tab title="INRIA Asclepios, France" disabled="no" anchor="" url="" target="blank" class=""] Inria Asclepios Desription to complete Contacts: Nicholas Ayache [/su_tabs] [su_tab title="ENS de Cachan, France" disabled="no" anchor="" url="" target="blank" class=""] ENS de Cachan Description to complete Contact: Alain Trouvé [/su_tabs] [su_tab title="" disabled="no" anchor="" url="" target="blank" class=""] Where Description to complete Who [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 11:31:30', '2018-11-19 10:31:30', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1370, 7, '2018-11-19 11:32:02', '2018-11-19 10:32:02', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""] Center for Medical Image Computing Description to complete Contacts: Sébastien Ourselin, Daniel Alexander[/su_tab] [su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""] Scientific Computing and Imaging (SCI) Institute, University of Utah, USA Description to complete Contacts: Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab] [su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""] Departement of Physics. Queen Mary University of London, UK Description to complete Contacts: Vito Latora[/su_tab][/su_tabs] [su_tab title="University of Minnesota" disabled="no" anchor="" url="" target="blank" class=""] Center for Magnetic Resonance Research, University of Minnesota, USA Description to complete Contacts : Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil[/su_tabs] [su_tab title="INRIA Asclepios, France" disabled="no" anchor="" url="" target="blank" class=""] Inria Asclepios Desription to complete Contacts: Nicholas Ayache[/su_tabs] [su_tab title="ENS de Cachan, France" disabled="no" anchor="" url="" target="blank" class=""] ENS de Cachan Description to complete Contact: Alain Trouvé [/su_tabs] [su_tab title="" disabled="no" anchor="" url="" target="blank" class=""] Where Description to complete Who [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 11:32:02', '2018-11-19 10:32:02', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1371, 7, '2018-11-19 11:32:35', '2018-11-19 10:32:35', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""] Center for Medical Image Computing Description to complete Contacts: Sébastien Ourselin, Daniel Alexander[/su_tab] [su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""] Scientific Computing and Imaging (SCI) Institute, University of Utah, USA Description to complete Contacts: Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab] [su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""] Departement of Physics. Queen Mary University of London, UK Description to complete Contacts: Vito Latora[/su_tab] [su_tab title="University of Minnesota" disabled="no" anchor="" url="" target="blank" class=""] Center for Magnetic Resonance Research, University of Minnesota, USA Description to complete Contacts : Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil[/su_tabs] [su_tab title="INRIA Asclepios, France" disabled="no" anchor="" url="" target="blank" class=""] Inria Asclepios Desription to complete Contacts: Nicholas Ayache[/su_tabs] [su_tab title="ENS de Cachan, France" disabled="no" anchor="" url="" target="blank" class=""] ENS de Cachan Description to complete Contact: Alain Trouvé [/su_tabs] [su_tab title="" disabled="no" anchor="" url="" target="blank" class=""] Where Description to complete Who [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 11:32:35', '2018-11-19 10:32:35', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1372, 7, '2018-11-19 11:33:03', '2018-11-19 10:33:03', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""] Center for Medical Image Computing Description to complete Contacts: Sébastien Ourselin, Daniel Alexander[/su_tab] [su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""] Scientific Computing and Imaging (SCI) Institute, University of Utah, USA Description to complete Contacts: Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab] [su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""] Departement of Physics. Queen Mary University of London, UK Description to complete Contacts: Vito Latora[/su_tab] [su_tab title="University of Minnesota" disabled="no" anchor="" url="" target="blank" class=""] Center for Magnetic Resonance Research, University of Minnesota, USA Description to complete Contacts : Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil[/su_tab] [su_tab title="INRIA Asclepios, France" disabled="no" anchor="" url="" target="blank" class=""] Inria Asclepios Desription to complete Contacts: Nicholas Ayache[/su_tab] [su_tab title="ENS de Cachan, France" disabled="no" anchor="" url="" target="blank" class=""] ENS de Cachan Description to complete Contact: Alain Trouvé [/su_tab] [su_tab title="" disabled="no" anchor="" url="" target="blank" class=""] Where Description to complete Who [/su_tab] [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 11:33:03', '2018-11-19 10:33:03', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1373, 7, '2018-11-19 11:35:58', '2018-11-19 10:35:58', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""] Center for Medical Image Computing Description to complete Contacts: Sébastien Ourselin, Daniel Alexander[/su_tab] [su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""] Scientific Computing and Imaging (SCI) Institute, University of Utah, USA Description to complete Contacts: Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab] [su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""] Departement of Physics. Queen Mary University of London, UK Description to complete Contacts: Vito Latora[/su_tab] [su_tab title="University of Minnesota" disabled="no" anchor="" url="" target="blank" class=""] Center for Magnetic Resonance Research, University of Minnesota, USA Description to complete Contacts : Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil[/su_tab] [su_tab title="INRIA Asclepios, France" disabled="no" anchor="" url="" target="blank" class=""] Inria Asclepios Desription to complete Contacts: Nicholas Ayache[/su_tab] [su_tab title="ENS de Cachan, France" disabled="no" anchor="" url="" target="blank" class=""] ENS de Cachan Description to complete Contact: Alain Trouvé [/su_tab] [su_tab title="University Paris-Descartes, France" disabled="no" anchor="" url="" target="blank" class=""] Université Paris-Descartes Contact: Joan Glaunès [/su_tab] [su_tab title="University Paul Sabatier, France" disabled="no" anchor="" url="" target="blank" class=""] Laboratoire AMIS, Université Paul Sabatier, Toulouse Description to complete Contacts: José Braga, Jean Dumoncel [/su_tab] [su_tab title="Institut Pasteur, France" disabled="no" anchor="" url="" target="blank" class=""] Institut Pasteur, Paris (Roberto Toro [/su_tab] [su_tab title="INSEAD, France" disabled="no" anchor="" url="" target="blank" class=""] INSEAD, Fontainebleau (Theodoros Evgeniou) [/su_tab] [su_tab title="CEA Neurospin, France" disabled="no" anchor="" url="" target="blank" class=""] Neurospin (Jean-François Mangin, Alexandre Vignaud, Vincent Frouin, Lucie Hertz-Pannier) [/su_tab] [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 11:35:58', '2018-11-19 10:35:58', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1374, 7, '2018-11-19 11:39:26', '2018-11-19 10:39:26', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""] Center for Medical Image Computing Description to complete Contacts: Sébastien Ourselin, Daniel Alexander[/su_tab] [su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""] Scientific Computing and Imaging (SCI) Institute, University of Utah, USA Description to complete Contacts: Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab] [su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""] Departement of Physics. Queen Mary University of London, UK Description to complete Contacts: Vito Latora[/su_tab] [su_tab title="University of Minnesota" disabled="no" anchor="" url="" target="blank" class=""] Center for Magnetic Resonance Research, University of Minnesota, USA Description to complete Contacts : Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil[/su_tab] [su_tab title="INRIA Asclepios, France" disabled="no" anchor="" url="" target="blank" class=""] Inria Asclepios Desription to complete Contacts: Nicholas Ayache[/su_tab] [su_tab title="ENS de Cachan, France" disabled="no" anchor="" url="" target="blank" class=""] ENS de Cachan Description to complete Contact: Alain Trouvé [/su_tab] [su_tab title="University Paris-Descartes, France" disabled="no" anchor="" url="" target="blank" class=""] Université Paris-Descartes Contact: Joan Glaunès [/su_tab] [su_tab title="University Paul Sabatier, France" disabled="no" anchor="" url="" target="blank" class=""] Laboratoire AMIS, Université Paul Sabatier, Toulouse Description to complete Contacts: José Braga, Jean Dumoncel [/su_tab] [su_tab title="Institut Pasteur, France" disabled="no" anchor="" url="" target="blank" class=""] Institut Pasteur, Paris (Roberto Toro [/su_tab] [su_tab title="INSEAD, France" disabled="no" anchor="" url="" target="blank" class=""] INSEAD, Fontainebleau (Theodoros Evgeniou) [/su_tab] [su_tab title="CEA Neurospin, France" disabled="no" anchor="" url="" target="blank" class=""] Neurospin (Jean-François Mangin, Alexandre Vignaud, Vincent Frouin, Lucie Hertz-Pannier) [/su_tab] [/su_tabs] [su_tabs vertical="yes"] [su_tab title="Sainte Anne hostpital, Paris, France" disabled="no" anchor="" url="" target="blank" class=""] Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin) [/su_tab] [su_tab title="Bordeaux university hospital" disabled="no" anchor="" url="" target="blank" class=""] Bordeaux University Hospital (Carole Dufouil) [/su_tab] [su_tab title="Cean University Hospital" disabled="no" anchor="" url="" target="blank" class=""] Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges) [/su_tab] [su_tab title="Lille University Hospital" disabled="no" anchor="" url="" target="blank" class=""] Lille University Hospital (Christine Delmaire) [/su_tab] [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 11:39:26', '2018-11-19 10:39:26', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1375, 7, '2018-11-19 11:41:09', '2018-11-19 10:41:09', '[su_slider source="media: 968,966,1015,958,971" width="1600" height="600" title="no" mousewheel="no"]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
  [caption id="attachment_1227" align="center"]
Team retreat at the Villa Finaly, Florence, Italy
[/caption] Team retreat at the villa Finaly, Florence, Italy', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-19 11:41:09', '2018-11-19 10:41:09', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1376, 7, '2018-11-19 11:41:25', '2018-11-19 10:41:25', '[su_slider source="media: 968,966,1015,958,971" width="1600" height="600" title="no" mousewheel="no"]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
  [caption id="attachment_1227" align="center"]
Team retreat at the Villa Finaly, Florence, Italy
[/caption]
Team retreat at the villa Finaly, Florence, Italy
', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-19 11:41:25', '2018-11-19 10:41:25', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1377, 7, '2018-11-19 11:41:45', '2018-11-19 10:41:45', '[su_slider source="media: 968,966,1015,958,971" width="1600" height="600" title="no" mousewheel="no"]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
  [caption id="attachment_1227" align="center"]
Team retreat at the Villa Finaly, Florence, Italy
[/caption]
Team retreat at the villa Finaly, Florence, Italy
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Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to: i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button] [su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="http://www.sciencedirect.com/science/article/pii/S1053811914005205" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 11:48:12', '2018-11-19 10:48:12', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1380, 7, '2018-11-19 11:48:54', '2018-11-19 10:48:54', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
i) apply advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make research more reproducible.
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button] [su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="http://www.sciencedirect.com/science/article/pii/S1053811914005205" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 11:48:54', '2018-11-19 10:48:54', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1381, 7, '2018-11-19 11:49:15', '2018-11-19 10:49:15', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.[/su_button] [su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="http://www.sciencedirect.com/science/article/pii/S1053811914005205" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-19 11:49:15', '2018-11-19 10:49:15', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1382, 7, '2018-11-19 11:56:46', '2018-11-19 10:56:46', '

Most representative publications

Clinica: an open source software platform for reproducible clinical neuroscience studiesRoutier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
[su_spoiler title="Spoiler title" style="fancy" icon="plus-circle"]Hidden content[/su_spoiler] [su_spoiler title="Spoiler title" style="wood" icon="plus-circle"]Hidden content[/su_spoiler] [su_spoiler title="Spoiler title" style="fabric" icon="plus-circle"]Hidden content[/su_spoiler] [su_spoiler title="Spoiler title" style="modern-violet" icon="plus-circle"]Hidden content[/su_spoiler] [su_spoiler title="Spoiler title" style="grid" icon="plus-circle"]Hidden content[/su_spoiler]
      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 11:56:46', '2018-11-19 10:56:46', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1383, 7, '2018-11-19 11:57:11', '2018-11-19 10:57:11', '

Most representative publications

Clinica: an open source software platform for reproducible clinical neuroscience studiesRoutier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
[su_spoiler title="Spoiler title" style="fancy" icon="plus-circle"]Hidden content[/su_spoiler] [su_spoiler title="Spoiler title" style="wood" icon="plus-circle"]Hidden content[/su_spoiler] [su_spoiler title="Spoiler title" style="fabric" icon="plus-circle"]Hidden content[/su_spoiler] [su_spoiler title="Spoiler title" style="modern-violet" icon="plus-circle"]Hidden content[/su_spoiler] [su_spoiler title="Spoiler title" style="simple" icon="plus-circle"]Hidden content[/su_spoiler]
      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 11:57:11', '2018-11-19 10:57:11', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1384, 7, '2018-11-19 11:57:46', '2018-11-19 10:57:46', '

Most representative publications

Clinica: an open source software platform for reproducible clinical neuroscience studiesRoutier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
[su_spoiler title="Clinica: an open source software platform for reproducible clinical neuroscience studies" style="fancy" icon="plus-circle"]Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.[/su_spoiler]
      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 11:57:46', '2018-11-19 10:57:46', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1385, 7, '2018-11-19 11:58:40', '2018-11-19 10:58:40', '

Most representative publications

Clinica: an open source software platform for reproducible clinical neuroscience studiesRoutier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O.
[su_spoiler title="Clinica: an open source software platform for reproducible clinical neuroscience studies" style="fancy" icon="plus-circle"] Routier A., Guillon J., Burgos N., Samper-Gonzalez J., Wen J., Fontanella S., Bottani S., Jacquemont T., Marcoux A., Gori P., Lu P., Moreau T., Bacci M., Durrleman S., Colliot O. In: Lorem Ipsum 2018[/su_spoiler]
      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-19 11:58:40', '2018-11-19 10:58:40', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1386, 7, '2018-11-19 12:01:01', '2018-11-19 11:01:01', '

Faculty

[tmm name="faculty"]

Collaborators

[tmm name="collaborators"]

Post-docs

[tmm name="post-docs"]

Engineers

[tmm name="engineers"]

PhD Students

[tmm name="phd-students"]

Master students

[tmm name="master-students"]

Former Members

  • Jeremy Guillon- Former PhD student - webpage
  • Michael Bacci
  • Sabrina Fontanella
  • Clementine Fourrier
  • Federico Battiston
  • Takoua Kaaouana
  • Xavier Navarro
  • Camille Chrétien
  • Hugo Boniface
  • Jungying Fang
  • Romain Valabrègue - Inserm Research Engineer - webpage - romain.valabregue@upmc.fr
  • Eric Bardinet - CNRS Research Engineer - webpage - eric.bardinet@upmc.fr
  • Sara Fernandez-Vidal - Inserm Research Engineer - webpage - sara.fernandez_vidal@upmc.fr
  • Vera Dinkelacker - Rothschild Foundation - v.dinkelacker@gmail.com
  • Thomas Estienne - UPMC, CATI project - thomas.estienne@icm-institute.org
  • Martina Sundqvist - Ecole Normale Supérieure de Paris
  • Thomas Jacquemont - Ecole Normale Supérieure de Paris
  • Aurore Bussalb - Institut Supérieur de BioSciences de Paris
  • Carlos Tor Diez - Télécom ParisTech
  • Ayoub Louati - Ecole Polytechnique de Tunisie
  • Paul Jusselin - Ecole Normale Supérieure de Cachan
  • Chabha Azouani - CATI project - chabha.azouani@upmc.fr
  • Kelly Martineau - CATI project – kelly.martineau@icm-institute.org
  • Sonia Djobeir - CATI project – soniadjobeir@gmail.com
  • Hugo Dary - CATI project - dary.hugo@gmail.com
  • Ludovic Fillon - CATI project - ludovic.fillon@upmc.fr
  • Mathieu Dubois - CATI project - mathieu.dubois@icm-institute.org
  • Barbara Gris - gris@cmla.ens-cachan.fr
  • Géraldine Rousseau - geraldine.rouseau@psl.ap-hop-paris.fr
  • Marika Rudler - marika.rudler@psl.aphp.fr
  • Jean-Baptiste Schiratti - jean-baptiste.schiratti@cmap.polytechnique.fr
  • Pietro Gori - Postdoctoral fellow - Now Assistant Professor, Telecom ParisTech
  • Ana Fouquier - - Postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Alexandre Pron - Master\'s student (Université Paris Descartes) - Now PhD student at INT, Marseille
  • Susovan Pal - Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Engineer - Now Pipeline TD Junior at Ellipse Studio
  • François Deloche - Intern (École Polytechnique)
  • Alexis Mocellin - Intern (Ecole Polytechnique)
  • Claire Cury - PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Clinical Research Associate - Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Clinical Research Associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Kanza Dekkiche - Master\'s student
  • Antoine Latrille - Master\'s student (ESIEE Paris) - Now Ingénieur Développeur Web at Easyvoyage
  • Guillaume Ruffin - Intern
  • Evgeny Zuenko - Master\'s student
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Maxime Corduant - Master\'s student
  • Kevin Roussel - Master\'s student
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP - claude.adam@psl.aphp.fr
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP - sophie.dupont@psl.aphp.fr
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP - damien.galanaud@icm-institute.org
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP - yves.samson@psl.aphp.fr
  • Lionel Thivard - Neurologist (PH), AP-HP - lionel.thivard@psl.aphp.fr
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
  [caption id="attachment_1227" align="center"]
Team retreat at the Villa Finaly, Florence, Italy
[/caption]
Team retreat at the villa Finaly, Florence, Italy
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
  [caption id="attachment_1227" align="center"]
Team retreat at the Villa Finaly, Florence, Italy
[/caption]
Team retreat at the villa Finaly, Florence, Italy
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Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button] [su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vido Fallani et al. 2017[/su_button] [su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""] Center for Medical Image Computing Description to complete Contacts: Sébastien Ourselin, Daniel Alexander[/su_tab] [su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""] Scientific Computing and Imaging (SCI) Institute, University of Utah, USA Description to complete Contacts: Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab] [su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""] Departement of Physics. Queen Mary University of London, UK Description to complete Contacts: Vito Latora[/su_tab] [su_tab title="University of Minnesota" disabled="no" anchor="" url="" target="blank" class=""] Center for Magnetic Resonance Research, University of Minnesota, USA Description to complete Contacts : Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil[/su_tab] [su_tab title="INRIA Asclepios, France" disabled="no" anchor="" url="" target="blank" class=""] Inria Asclepios Desription to complete Contacts: Nicholas Ayache[/su_tab] [su_tab title="ENS de Cachan, France" disabled="no" anchor="" url="" target="blank" class=""] ENS de Cachan Description to complete Contact: Alain Trouvé [/su_tab] [su_tab title="University Paris-Descartes, France" disabled="no" anchor="" url="" target="blank" class=""] Université Paris-Descartes Contact: Joan Glaunès [/su_tab] [su_tab title="University Paul Sabatier, France" disabled="no" anchor="" url="" target="blank" class=""] Laboratoire AMIS, Université Paul Sabatier, Toulouse Description to complete Contacts: José Braga, Jean Dumoncel [/su_tab] [su_tab title="Institut Pasteur, France" disabled="no" anchor="" url="" target="blank" class=""] Institut Pasteur, Paris (Roberto Toro [/su_tab] [su_tab title="INSEAD, France" disabled="no" anchor="" url="" target="blank" class=""] INSEAD, Fontainebleau (Theodoros Evgeniou) [/su_tab] [su_tab title="CEA Neurospin, France" disabled="no" anchor="" url="" target="blank" class=""] Neurospin (Jean-François Mangin, Alexandre Vignaud, Vincent Frouin, Lucie Hertz-Pannier) [/su_tab] [/su_tabs] [su_tabs vertical="yes"] [su_tab title="Sainte Anne hostpital, Paris, France" disabled="no" anchor="" url="" target="blank" class=""] Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin) [/su_tab] [su_tab title="Bordeaux university hospital" disabled="no" anchor="" url="" target="blank" class=""] Bordeaux University Hospital (Carole Dufouil) [/su_tab] [su_tab title="Cean University Hospital" disabled="no" anchor="" url="" target="blank" class=""] Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges) [/su_tab] [su_tab title="Lille University Hospital" disabled="no" anchor="" url="" target="blank" class=""] Lille University Hospital (Christine Delmaire) [/su_tab] [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-19 17:50:43', '2018-11-19 16:50:43', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1398, 7, '2018-11-19 17:52:24', '2018-11-19 16:52:24', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button] [su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017[/su_button] [su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""] Center for Medical Image Computing Description to complete Contacts: Sébastien Ourselin, Daniel Alexander[/su_tab] [su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""] Scientific Computing and Imaging (SCI) Institute, University of Utah, USA Description to complete Contacts: Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab] [su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""] Departement of Physics. Queen Mary University of London, UK Description to complete Contacts: Vito Latora[/su_tab] [su_tab title="University of Minnesota" disabled="no" anchor="" url="" target="blank" class=""] Center for Magnetic Resonance Research, University of Minnesota, USA Description to complete Contacts : Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil[/su_tab] [su_tab title="INRIA Asclepios, France" disabled="no" anchor="" url="" target="blank" class=""] Inria Asclepios Desription to complete Contacts: Nicholas Ayache[/su_tab] [su_tab title="ENS de Cachan, France" disabled="no" anchor="" url="" target="blank" class=""] ENS de Cachan Description to complete Contact: Alain Trouvé [/su_tab] [su_tab title="University Paris-Descartes, France" disabled="no" anchor="" url="" target="blank" class=""] Université Paris-Descartes Contact: Joan Glaunès [/su_tab] [su_tab title="University Paul Sabatier, France" disabled="no" anchor="" url="" target="blank" class=""] Laboratoire AMIS, Université Paul Sabatier, Toulouse Description to complete Contacts: José Braga, Jean Dumoncel [/su_tab] [su_tab title="Institut Pasteur, France" disabled="no" anchor="" url="" target="blank" class=""] Institut Pasteur, Paris (Roberto Toro [/su_tab] [su_tab title="INSEAD, France" disabled="no" anchor="" url="" target="blank" class=""] INSEAD, Fontainebleau (Theodoros Evgeniou) [/su_tab] [su_tab title="CEA Neurospin, France" disabled="no" anchor="" url="" target="blank" class=""] Neurospin (Jean-François Mangin, Alexandre Vignaud, Vincent Frouin, Lucie Hertz-Pannier) [/su_tab] [/su_tabs] [su_tabs vertical="yes"] [su_tab title="Sainte Anne hostpital, Paris, France" disabled="no" anchor="" url="" target="blank" class=""] Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin) [/su_tab] [su_tab title="Bordeaux university hospital" disabled="no" anchor="" url="" target="blank" class=""] Bordeaux University Hospital (Carole Dufouil) [/su_tab] [su_tab title="Cean University Hospital" disabled="no" anchor="" url="" target="blank" class=""] Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges) [/su_tab] [su_tab title="Lille University Hospital" disabled="no" anchor="" url="" target="blank" class=""] Lille University Hospital (Christine Delmaire) [/su_tab] [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

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Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button] [su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017[/su_button] [su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""] Center for Medical Image Computing Description to complete Contacts: Sébastien Ourselin, Daniel Alexander[/su_tab] [su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""] Scientific Computing and Imaging (SCI) Institute, University of Utah, USA Description to complete Contacts: Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab] [su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""] Departement of Physics. Queen Mary University of London, UK Description to complete Contacts: Vito Latora[/su_tab] [su_tab title="University of Minnesota" disabled="no" anchor="" url="" target="blank" class=""] Center for Magnetic Resonance Research, University of Minnesota, USA Description to complete Contacts : Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil[/su_tab] [su_tab title="INRIA Asclepios, France" disabled="no" anchor="" url="" target="blank" class=""] Inria Asclepios Desription to complete Contacts: Nicholas Ayache[/su_tab] [su_tab title="ENS de Cachan, France" disabled="no" anchor="" url="" target="blank" class=""] ENS de Cachan Description to complete Contact: Alain Trouvé [/su_tab] [su_tab title="University Paris-Descartes, France" disabled="no" anchor="" url="" target="blank" class=""] Université Paris-Descartes Contact: Joan Glaunès [/su_tab] [su_tab title="University Paul Sabatier, France" disabled="no" anchor="" url="" target="blank" class=""] Laboratoire AMIS, Université Paul Sabatier, Toulouse Description to complete Contacts: José Braga, Jean Dumoncel [/su_tab] [su_tab title="Institut Pasteur, France" disabled="no" anchor="" url="" target="blank" class=""] Institut Pasteur, Paris (Roberto Toro [/su_tab] [su_tab title="INSEAD, France" disabled="no" anchor="" url="" target="blank" class=""] INSEAD, Fontainebleau (Theodoros Evgeniou) [/su_tab] [su_tab title="CEA Neurospin, France" disabled="no" anchor="" url="" target="blank" class=""] Neurospin (Jean-François Mangin, Alexandre Vignaud, Vincent Frouin, Lucie Hertz-Pannier) [/su_tab] [/su_tabs] [su_tabs vertical="yes"] [su_tab title="Sainte Anne hostpital, Paris, France" disabled="no" anchor="" url="" target="blank" class=""] Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin) [/su_tab] [su_tab title="Bordeaux university hospital" disabled="no" anchor="" url="" target="blank" class=""] Bordeaux University Hospital (Carole Dufouil) [/su_tab] [su_tab title="Cean University Hospital" disabled="no" anchor="" url="" target="blank" class=""] Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges) [/su_tab] [su_tab title="Lille University Hospital" disabled="no" anchor="" url="" target="blank" class=""] Lille University Hospital (Christine Delmaire) [/su_tab] [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
  [caption id="attachment_1227" align="center"]
Team retreat at the Villa Finaly, Florence, Italy
[/caption]
Team retreat at the villa Finaly, Florence, Italy
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
  [caption id="attachment_1227" align="center"]
Team retreat at the Villa Finaly, Florence, Italy
[/caption]
Team retreat at the villa Finaly, Florence, Italy
', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-19 18:00:47', '2018-11-19 17:00:47', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1402, 7, '2018-11-19 18:01:19', '2018-11-19 17:01:19', '[su_slider source="media: 968,966,1015,958,971" width="1600" height="600" title="no" mousewheel="no"]
As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
  [caption id="attachment_1227" align="center"]
Team retreat at the Villa Finaly, Florence, Italy
[/caption]
Team retreat at the villa Finaly, Florence, Italy
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
  [caption id="attachment_1227" align="center"]
Team retreat at the Villa Finaly, Florence, Italy
[/caption]
Team retreat at the villa Finaly, Florence, Italy
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
  [caption id="attachment_1227" align="center"]
Team retreat at the Villa Finaly, Florence, Italy
[/caption]
Team retreat at the villa Finaly, Florence, Italy
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
  [caption id="attachment_1227" align="center"]
Team retreat at the Villa Finaly, Florence, Italy
[/caption]
Team retreat at the villa Finaly, Florence, Italy
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As a joint research team {CNRS, Inria, Inserm, UPMC}, AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
  [caption id="attachment_1227" align="center"]
Team retreat at the Villa Finaly, Florence, Italy
[/caption]
Team retreat at the villa Finaly, Florence, Italy
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As a joint research team (CNRS, Inria, Inserm, UPMC), AramisLab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
  [caption id="attachment_1227" align="center"]
Team retreat at the Villa Finaly, Florence, Italy
[/caption]
Team retreat at the villa Finaly, Florence, Italy
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Faculty

[tmm name="faculty"]

Post-docs

[tmm name="post-docs"]

Engineers

[tmm name="engineers"]

PhD Students

[tmm name="phd-students"]

Collaborators

[tmm name="collaborators"]

Former Members

  • Jeremy Guillon- Former PhD student - webpage
  • Michael Bacci
  • Sabrina Fontanella
  • Clementine Fourrier
  • Federico Battiston
  • Takoua Kaaouana
  • Xavier Navarro
  • Camille Chrétien
  • Hugo Boniface
  • Jungying Fang
  • Romain Valabrègue - Inserm Research Engineer - webpage - romain.valabregue@upmc.fr
  • Eric Bardinet - CNRS Research Engineer - webpage - eric.bardinet@upmc.fr
  • Sara Fernandez-Vidal - Inserm Research Engineer - webpage - sara.fernandez_vidal@upmc.fr
  • Vera Dinkelacker - Rothschild Foundation - v.dinkelacker@gmail.com
  • Thomas Estienne - UPMC, CATI project - thomas.estienne@icm-institute.org
  • Martina Sundqvist - Ecole Normale Supérieure de Paris
  • Thomas Jacquemont - Ecole Normale Supérieure de Paris
  • Aurore Bussalb - Institut Supérieur de BioSciences de Paris
  • Carlos Tor Diez - Télécom ParisTech
  • Ayoub Louati - Ecole Polytechnique de Tunisie
  • Paul Jusselin - Ecole Normale Supérieure de Cachan
  • Chabha Azouani - CATI project - chabha.azouani@upmc.fr
  • Kelly Martineau - CATI project – kelly.martineau@icm-institute.org
  • Sonia Djobeir - CATI project – soniadjobeir@gmail.com
  • Hugo Dary - CATI project - dary.hugo@gmail.com
  • Ludovic Fillon - CATI project - ludovic.fillon@upmc.fr
  • Mathieu Dubois - CATI project - mathieu.dubois@icm-institute.org
  • Barbara Gris - gris@cmla.ens-cachan.fr
  • Géraldine Rousseau - geraldine.rouseau@psl.ap-hop-paris.fr
  • Marika Rudler - marika.rudler@psl.aphp.fr
  • Jean-Baptiste Schiratti - jean-baptiste.schiratti@cmap.polytechnique.fr
  • Pietro Gori - Postdoctoral fellow - Now Assistant Professor, Telecom ParisTech
  • Ana Fouquier - - Postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Alexandre Pron - Master\'s student (Université Paris Descartes) - Now PhD student at INT, Marseille
  • Susovan Pal - Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Engineer - Now Pipeline TD Junior at Ellipse Studio
  • François Deloche - Intern (École Polytechnique)
  • Alexis Mocellin - Intern (Ecole Polytechnique)
  • Claire Cury - PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Clinical Research Associate - Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Clinical Research Associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Kanza Dekkiche - Master\'s student
  • Antoine Latrille - Master\'s student (ESIEE Paris) - Now Ingénieur Développeur Web at Easyvoyage
  • Guillaume Ruffin - Intern
  • Evgeny Zuenko - Master\'s student
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Maxime Corduant - Master\'s student
  • Kevin Roussel - Master\'s student
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP - claude.adam@psl.aphp.fr
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP - sophie.dupont@psl.aphp.fr
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP - damien.galanaud@icm-institute.org
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP - yves.samson@psl.aphp.fr
  • Lionel Thivard - Neurologist (PH), AP-HP - lionel.thivard@psl.aphp.fr
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Most representative publications

      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-20 12:08:43', '2018-11-20 11:08:43', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1415, 7, '2018-11-20 12:10:32', '2018-11-20 11:10:32', '

Most representative publications

Networks

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-20 12:10:32', '2018-11-20 11:10:32', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1416, 7, '2018-11-20 12:11:05', '2018-11-20 11:11:05', '

Publications

Networks

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-20 12:11:05', '2018-11-20 11:11:05', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1417, 7, '2018-11-20 12:11:30', '2018-11-20 11:11:30', '

Networks

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-20 12:11:30', '2018-11-20 11:11:30', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1418, 7, '2018-11-20 12:11:48', '2018-11-20 11:11:48', '

Networks

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
      • Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, Wiggins C, Vignaud A, Hasboun D, Defontaines B, Hanon O, Dubois B, Sarazin M, Hertz-Pannier L, Colliot O. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical 5:341–348, 2014. PDF Paper in PDF
      • Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert MO, Chupin M, Benali H, Colliot O. The ADNI. Automatic classification of patients with Alzheimer\'s disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 15;56(2):766-81, 2011. PDF Paper in PDF
      • Cuingnet R, Rosso C, Chupin M, Lehéricy S, Dormont D, Benali H, Samson Y, Colliot O, Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome, Medical Image Analysis 15(5):729-37, 2011.PDF Paper in PDF
      • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
      • Cury C, Glaunès JA, Colliot O, Diffeomorphic iterative centroid methods for template estimation on large datasets, In Geometric Theory of Information, F. Nielsen (editor), pp 273-300, Springer, 2014PDF Paper in PDF
      • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
      • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
      • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
      • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
      • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, De Vico Fallani F, Chavez M, Poupon C, Hartmann A, Ayache N, Durrleman S. Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles. IEEE Transactions on Medical Imaging, 35(12), 2609-2619.PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Routier A, Poupon C, Hartmann A, Ayache N, Durrleman S. Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms. In Information Processing in Medical Imaging pp. 275–287, 2015. PDF Paper in PDF
      • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
      • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-20 12:11:48', '2018-11-20 11:11:48', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1419, 7, '2018-11-20 12:12:49', '2018-11-20 11:12:49', '

Faculty

[tmm name="faculty"]

Post-docs

[tmm name="post-docs"]

Engineers

[tmm name="engineers"]

PhD Students

[tmm name="phd-students"]

Associate fellows

[tmm name="collaborators"]

Former Members

  • Jeremy Guillon- Former PhD student - webpage
  • Michael Bacci
  • Sabrina Fontanella
  • Clementine Fourrier
  • Federico Battiston
  • Takoua Kaaouana
  • Xavier Navarro
  • Camille Chrétien
  • Hugo Boniface
  • Jungying Fang
  • Romain Valabrègue - Inserm Research Engineer - webpage - romain.valabregue@upmc.fr
  • Eric Bardinet - CNRS Research Engineer - webpage - eric.bardinet@upmc.fr
  • Sara Fernandez-Vidal - Inserm Research Engineer - webpage - sara.fernandez_vidal@upmc.fr
  • Vera Dinkelacker - Rothschild Foundation - v.dinkelacker@gmail.com
  • Thomas Estienne - UPMC, CATI project - thomas.estienne@icm-institute.org
  • Martina Sundqvist - Ecole Normale Supérieure de Paris
  • Thomas Jacquemont - Ecole Normale Supérieure de Paris
  • Aurore Bussalb - Institut Supérieur de BioSciences de Paris
  • Carlos Tor Diez - Télécom ParisTech
  • Ayoub Louati - Ecole Polytechnique de Tunisie
  • Paul Jusselin - Ecole Normale Supérieure de Cachan
  • Chabha Azouani - CATI project - chabha.azouani@upmc.fr
  • Kelly Martineau - CATI project – kelly.martineau@icm-institute.org
  • Sonia Djobeir - CATI project – soniadjobeir@gmail.com
  • Hugo Dary - CATI project - dary.hugo@gmail.com
  • Ludovic Fillon - CATI project - ludovic.fillon@upmc.fr
  • Mathieu Dubois - CATI project - mathieu.dubois@icm-institute.org
  • Barbara Gris - gris@cmla.ens-cachan.fr
  • Géraldine Rousseau - geraldine.rouseau@psl.ap-hop-paris.fr
  • Marika Rudler - marika.rudler@psl.aphp.fr
  • Jean-Baptiste Schiratti - jean-baptiste.schiratti@cmap.polytechnique.fr
  • Pietro Gori - Postdoctoral fellow - Now Assistant Professor, Telecom ParisTech
  • Ana Fouquier - - Postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Alexandre Pron - Master\'s student (Université Paris Descartes) - Now PhD student at INT, Marseille
  • Susovan Pal - Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Engineer - Now Pipeline TD Junior at Ellipse Studio
  • François Deloche - Intern (École Polytechnique)
  • Alexis Mocellin - Intern (Ecole Polytechnique)
  • Claire Cury - PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Clinical Research Associate - Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Clinical Research Associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Kanza Dekkiche - Master\'s student
  • Antoine Latrille - Master\'s student (ESIEE Paris) - Now Ingénieur Développeur Web at Easyvoyage
  • Guillaume Ruffin - Intern
  • Evgeny Zuenko - Master\'s student
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Maxime Corduant - Master\'s student
  • Kevin Roussel - Master\'s student
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP - claude.adam@psl.aphp.fr
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP - sophie.dupont@psl.aphp.fr
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP - damien.galanaud@icm-institute.org
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP - yves.samson@psl.aphp.fr
  • Lionel Thivard - Neurologist (PH), AP-HP - lionel.thivard@psl.aphp.fr
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2018-11-20 12:12:49', '2018-11-20 11:12:49', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '', (1420, 7, '2018-11-20 12:16:00', '2018-11-20 11:16:00', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.

References

  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"][/su_button] [su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="http://www.sciencedirect.com/science/article/pii/S1053811914005205" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:16:00', '2018-11-20 11:16:00', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1421, 7, '2018-11-20 12:16:19', '2018-11-20 11:16:19', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.

References

  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017.
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="https://hal.inria.fr/hal-01518785" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"][/su_button] [su_button url="https://hal.inria.fr/hal-01654000/" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="http://www.sciencedirect.com/science/article/pii/S1053811914005205" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:16:19', '2018-11-20 11:16:19', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1422, 7, '2018-11-20 12:17:44', '2018-11-20 11:17:44', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.

References

  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="http://www.sciencedirect.com/science/article/pii/S1053811914005205" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:17:44', '2018-11-20 11:17:44', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1423, 7, '2018-11-20 12:18:04', '2018-11-20 11:18:04', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="http://www.sciencedirect.com/science/article/pii/S1053811914005205" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:18:04', '2018-11-20 11:18:04', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1424, 7, '2018-11-20 12:18:33', '2018-11-20 11:18:33', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.

References

  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper

[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="http://www.sciencedirect.com/science/article/pii/S1053811914005205" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014.[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:18:33', '2018-11-20 11:18:33', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1425, 7, '2018-11-20 12:19:47', '2018-11-20 11:19:47', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.

References

  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper

[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. p>References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper

[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"].[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:19:47', '2018-11-20 11:19:47', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1426, 7, '2018-11-20 12:20:15', '2018-11-20 11:20:15', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.

References

  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper

[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. p>References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper

[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:20:15', '2018-11-20 11:20:15', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1427, 7, '2018-11-20 12:20:41', '2018-11-20 11:20:41', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:20:41', '2018-11-20 11:20:41', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1428, 7, '2018-11-20 12:25:00', '2018-11-20 11:25:00', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:25:00', '2018-11-20 11:25:00', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1429, 7, '2018-11-20 12:25:30', '2018-11-20 11:25:30', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:25:30', '2018-11-20 11:25:30', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1430, 7, '2018-11-20 12:25:53', '2018-11-20 11:25:53', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:25:53', '2018-11-20 11:25:53', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1431, 7, '2018-11-20 12:26:44', '2018-11-20 11:26:44', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
  • References
    • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
    References
    • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
    • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
    [su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
    [su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:26:44', '2018-11-20 11:26:44', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1432, 7, '2018-11-20 12:26:50', '2018-11-20 11:26:50', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:26:50', '2018-11-20 11:26:50', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1433, 7, '2018-11-20 12:30:29', '2018-11-20 11:30:29', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:30:29', '2018-11-20 11:30:29', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1434, 7, '2018-11-20 12:30:59', '2018-11-20 11:30:59', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:30:59', '2018-11-20 11:30:59', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1435, 7, '2018-11-20 12:31:31', '2018-11-20 11:31:31', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:31:31', '2018-11-20 11:31:31', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1436, 7, '2018-11-20 12:31:52', '2018-11-20 11:31:52', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:31:52', '2018-11-20 11:31:52', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1437, 7, '2018-11-20 12:32:12', '2018-11-20 11:32:12', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:32:12', '2018-11-20 11:32:12', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1438, 7, '2018-11-20 12:32:30', '2018-11-20 11:32:30', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:32:30', '2018-11-20 11:32:30', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1439, 7, '2018-11-20 12:33:23', '2018-11-20 11:33:23', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:33:23', '2018-11-20 11:33:23', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1440, 7, '2018-11-20 12:33:42', '2018-11-20 11:33:42', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:33:42', '2018-11-20 11:33:42', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1441, 7, '2018-11-20 12:33:57', '2018-11-20 11:33:57', '

Clinica

Clinica
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:33:57', '2018-11-20 11:33:57', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1442, 7, '2018-11-20 12:34:27', '2018-11-20 11:34:27', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:34:27', '2018-11-20 11:34:27', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', 0); INSERT INTO `wp_aramis_posts` VALUES (1443, 7, '2018-11-20 12:34:44', '2018-11-20 11:34:44', '

Clinica

Clinica
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinica

Clinica
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:34:44', '2018-11-20 11:34:44', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1444, 7, '2018-11-20 12:35:31', '2018-11-20 11:35:31', '

Clinica

Clinica
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:35:31', '2018-11-20 11:35:31', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1445, 7, '2018-11-20 12:35:52', '2018-11-20 11:35:52', '

Clinica

Clinica
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]To complete[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:35:52', '2018-11-20 11:35:52', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1446, 7, '2018-11-20 12:36:06', '2018-11-20 11:36:06', '

Clinica

Clinica
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:36:06', '2018-11-20 11:36:06', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1447, 7, '2018-11-20 12:38:24', '2018-11-20 11:38:24', '

Clinica

Clinica
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Leasp

To complete References
  • To complete
[su_button url="https://gitlab.icm-institute.org/aramislab/leasp" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman[at]inria[dot]fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:38:24', '2018-11-20 11:38:24', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1448, 7, '2018-11-20 12:38:56', '2018-11-20 11:38:56', '

Clinica

Clinica
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Leasp

To complete References
  • To complete
[su_button url="https://gitlab.icm-institute.org/aramislab/leasp" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(at)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:38:56', '2018-11-20 11:38:56', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1449, 7, '2018-11-20 12:39:09', '2018-11-20 11:39:09', '

Clinica

Clinica
Clinica is a software platform for multimodal brain image analysis in clinical research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies. For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging modalities: currently MRI (anatomical, functional, diffusion) and PET, in the future, EEG/MEG.
For each modality, Clinica allows to easily extract various types of features (regional measures, parametric maps, surfaces, curves, networks). Such features are then subsequently used as input of machine learning, statistical modeling, morphometry or network analysis methods. Clinica also provides an integrated data management specification to store raw and processing data. Overall, Clinica helps to:
  • apply advanced analysis tools to clinical research studies
  • easily share data and results
  • make research more reproducible.
References
  • T. Jacquemont, et al. Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiology of Aging. Elsevier, 2017. Link to paper
  • A. Bertrand, et al. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. JAMA Neurol. 2017. Link to paper
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Leasp

To complete References
  • To complete
[su_button url="https://gitlab.icm-institute.org/aramislab/leasp" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(at)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]

', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-20 12:39:09', '2018-11-20 11:39:09', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1450, 7, '2018-11-20 12:40:43', '2018-11-20 11:40:43', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button] [su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017[/su_button] [su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""] Center for Medical Image Computing Description to complete Contacts: Sébastien Ourselin, Daniel Alexander[/su_tab] [su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""] Scientific Computing and Imaging (SCI) Institute, University of Utah, USA Description to complete Contacts: Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab] [su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""] Departement of Physics. Queen Mary University of London, UK Description to complete Contacts: Vito Latora[/su_tab] [su_tab title="University of Minnesota" disabled="no" anchor="" url="" target="blank" class=""] Center for Magnetic Resonance Research, University of Minnesota, USA Description to complete Contacts : Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil[/su_tab] [su_tab title="INRIA Asclepios, France" disabled="no" anchor="" url="" target="blank" class=""] Inria Asclepios Desription to complete Contacts: Nicholas Ayache[/su_tab] [su_tab title="ENS de Cachan, France" disabled="no" anchor="" url="" target="blank" class=""] ENS de Cachan Description to complete Contact: Alain Trouvé [/su_tab] [su_tab title="University Paris-Descartes, France" disabled="no" anchor="" url="" target="blank" class=""] Université Paris-Descartes Contact: Joan Glaunès [/su_tab] [su_tab title="University Paul Sabatier, France" disabled="no" anchor="" url="" target="blank" class=""] Laboratoire AMIS, Université Paul Sabatier, Toulouse Description to complete Contacts: José Braga, Jean Dumoncel [/su_tab] [su_tab title="Institut Pasteur, France" disabled="no" anchor="" url="" target="blank" class=""] Institut Pasteur, Paris (Roberto Toro [/su_tab] [su_tab title="INSEAD, France" disabled="no" anchor="" url="" target="blank" class=""] INSEAD, Fontainebleau (Theodoros Evgeniou) [/su_tab] [su_tab title="CEA Neurospin, France" disabled="no" anchor="" url="" target="blank" class=""] Neurospin (Jean-François Mangin, Alexandre Vignaud, Vincent Frouin, Lucie Hertz-Pannier) [/su_tab] [/su_tabs]
[su_tabs vertical="yes"] [su_tab title="Sainte Anne hostpital, Paris, France" disabled="no" anchor="" url="" target="blank" class=""] Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin) [/su_tab] [su_tab title="Bordeaux university hospital" disabled="no" anchor="" url="" target="blank" class=""] Bordeaux University Hospital (Carole Dufouil) [/su_tab] [su_tab title="Cean University Hospital" disabled="no" anchor="" url="" target="blank" class=""] Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges) [/su_tab] [su_tab title="Lille University Hospital" disabled="no" anchor="" url="" target="blank" class=""] Lille University Hospital (Christine Delmaire) [/su_tab] [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-20 12:40:43', '2018-11-20 11:40:43', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1451, 7, '2018-11-20 12:42:02', '2018-11-20 11:42:02', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button] [su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017[/su_button] [su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="University College London, UK" disabled="no" anchor="" url="" target="blank" class=""] [/su_tab] [su_tab title="University of Utah, USA" disabled="no" anchor="" url="" target="blank" class=""] Scientific Computing and Imaging (SCI) Institute, University of Utah, USA Description to complete Contacts: Guido Gerig, Sarang Joshi, Marcel Prastawa[/su_tab] [su_tab title="Queen Mary University of London, UK" disabled="no" anchor="" url="" target="blank" class=""] Departement of Physics. Queen Mary University of London, UK Description to complete Contacts: Vito Latora[/su_tab] [su_tab title="University of Minnesota" disabled="no" anchor="" url="" target="blank" class=""] Center for Magnetic Resonance Research, University of Minnesota, USA Description to complete Contacts : Pierre-François Van de Moortele, Tom Henry, Kamil Ugurbil[/su_tab] [su_tab title="INRIA Asclepios, France" disabled="no" anchor="" url="" target="blank" class=""] Inria Asclepios Desription to complete Contacts: Nicholas Ayache[/su_tab] [su_tab title="ENS de Cachan, France" disabled="no" anchor="" url="" target="blank" class=""] ENS de Cachan Description to complete Contact: Alain Trouvé [/su_tab] [su_tab title="University Paris-Descartes, France" disabled="no" anchor="" url="" target="blank" class=""] Université Paris-Descartes Contact: Joan Glaunès [/su_tab] [su_tab title="University Paul Sabatier, France" disabled="no" anchor="" url="" target="blank" class=""] Laboratoire AMIS, Université Paul Sabatier, Toulouse Description to complete Contacts: José Braga, Jean Dumoncel [/su_tab] [su_tab title="Institut Pasteur, France" disabled="no" anchor="" url="" target="blank" class=""] Institut Pasteur, Paris (Roberto Toro [/su_tab] [su_tab title="INSEAD, France" disabled="no" anchor="" url="" target="blank" class=""] INSEAD, Fontainebleau (Theodoros Evgeniou) [/su_tab] [su_tab title="CEA Neurospin, France" disabled="no" anchor="" url="" target="blank" class=""] Neurospin (Jean-François Mangin, Alexandre Vignaud, Vincent Frouin, Lucie Hertz-Pannier) [/su_tab] [/su_tabs]
[su_tabs vertical="yes"] [su_tab title="Sainte Anne hostpital, Paris, France" disabled="no" anchor="" url="" target="blank" class=""] Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin) [/su_tab] [su_tab title="Bordeaux university hospital" disabled="no" anchor="" url="" target="blank" class=""] Bordeaux University Hospital (Carole Dufouil) [/su_tab] [su_tab title="Cean University Hospital" disabled="no" anchor="" url="" target="blank" class=""] Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges) [/su_tab] [su_tab title="Lille University Hospital" disabled="no" anchor="" url="" target="blank" class=""] Lille University Hospital (Christine Delmaire) [/su_tab] [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-20 12:42:02', '2018-11-20 11:42:02', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1452, 7, '2018-11-20 12:44:41', '2018-11-20 11:44:41', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button] [su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017[/su_button] [su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_tabs vertical="yes"] [su_tab title="Institut Pasteur, France" disabled="no" anchor="" url="" target="blank" class=""] Institut Pasteur, Paris (Roberto Toro [/su_tab] [su_tab title="INSEAD, France" disabled="no" anchor="" url="" target="blank" class=""] INSEAD, Fontainebleau (Theodoros Evgeniou) [/su_tab] [su_tab title="CEA Neurospin, France" disabled="no" anchor="" url="" target="blank" class=""] Neurospin (Jean-François Mangin, Alexandre Vignaud, Vincent Frouin, Lucie Hertz-Pannier) [/su_tab] [/su_tabs]
[su_tabs vertical="yes"] [su_tab title="Sainte Anne hostpital, Paris, France" disabled="no" anchor="" url="" target="blank" class=""] Sainte-Anne Hospital, Paris (Catherine Oppenheim, Marie Sarazin) [/su_tab] [su_tab title="Bordeaux university hospital" disabled="no" anchor="" url="" target="blank" class=""] Bordeaux University Hospital (Carole Dufouil) [/su_tab] [su_tab title="Cean University Hospital" disabled="no" anchor="" url="" target="blank" class=""] Cycéron, Caen University Hospital (Gaël Chételat, Francis Eustache, Béatrice Desgranges) [/su_tab] [su_tab title="Lille University Hospital" disabled="no" anchor="" url="" target="blank" class=""] Lille University Hospital (Christine Delmaire) [/su_tab] [/su_tabs] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-20 12:44:41', '2018-11-20 11:44:41', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1453, 7, '2018-11-20 12:46:04', '2018-11-20 11:46:04', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_button url="#" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al.[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button] [su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017[/su_button] [su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-20 12:46:04', '2018-11-20 11:46:04', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1454, 7, '2018-11-20 12:47:00', '2018-11-20 11:47:00', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.

Reference


  • Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button] [su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017[/su_button] [su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-20 12:47:00', '2018-11-20 11:47:00', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1455, 7, '2018-11-20 12:48:01', '2018-11-20 11:48:01', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.

References


  • Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al. NIPS. Paper
New representations from multimodal medical images
Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button] [su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017[/su_button] [su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-20 12:48:01', '2018-11-20 11:48:01', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1456, 7, '2018-11-20 12:48:22', '2018-11-20 11:48:22', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.

References


  • Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al. NIPS. Paper
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_button url="https://arxiv.org/pdf/1807.05616.pdf" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018[/su_button] [su_button url="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005305" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017[/su_button] [su_button url="http://rstb.royalsocietypublishing.org/content/369/1653/20130521.short" size="3" style="ghost" background="#702082" color="#702082" center="yes" wide="yes"]Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014[/su_button] [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-20 12:48:22', '2018-11-20 11:48:22', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1457, 7, '2018-11-20 12:50:07', '2018-11-20 11:50:07', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.

References


  • Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al. NIPS. Paper
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.

References


[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).

References


  • Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018. Paper
  • A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017Paper
  • Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014Paper
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-20 12:50:07', '2018-11-20 11:50:07', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1458, 7, '2018-11-20 12:50:58', '2018-11-20 11:50:58', '

Context and general aim

The extraordinary progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of the brain disease development in living patients. Collection of multimodal data in large patient databases provide a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data : neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative disease (Alzheimer\'s disease and other dementia, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.

References


  • Learning spatiotemporal trajectories from manifold-valued longitudinal data. Schiratti et al. NIPS. Paper
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.

References


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Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).

References


  • Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018. Paper
  • A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017. Paper
  • Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014. Paper
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Spatio-temporal models to build trajectories of disease progression from longitudinal data

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Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.

References


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External collaborations

Methodological collaborations

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Local collaborations

Methodological collaborations

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Fundings

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The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numerical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University and belongs to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
We are financially supported by these institutions.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical learning, Deep learning, Generative models, Bayesian models [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Magnetic resonance Imaging (MRI), Positron Emission Tomography (PET)[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Riemannian Geometry, diffeomorphisms[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Graph analysis, Multilayer networks, Network dynamics[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Non-linear mixed effects models, Riemannian geometry, Longitudinal shape models[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Clinical decision support systems, Disease progression models, Network alterations[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Modeling the presymptomatic phase in genetic forms of the disease[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Using multimodal neuroimaging to track disease progression[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Modeling disease progression, Computer-aided adjustment of treatment[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Using BCI for rehabilitation, BCI systems based on network analysis[/su_spoiler] [/su_accordion]
 
Team retreat at the villa Finaly, Florence, Italy
 ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-autosave-v1', '', '', '2018-12-05 16:21:05', '2018-12-05 15:21:05', '', 1098, 'https://www.aramislab.fr/1098-autosave-v1/', 0, 'revision', '', (1461, 1, '2018-11-21 17:01:12', '2018-11-21 16:01:12', '[su_slider source="media: 968,966,1015,958,971" width="1600" height="600" title="no" mousewheel="no"]
The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
As a joint research team (CNRS, Inria, Inserm, UPMC), The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
 
Team retreat at the Villa Finaly, Florence, Italy
Team retreat at the villa Finaly, Florence, Italy
', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-21 17:01:12', '2018-11-21 16:01:12', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1462, 1, '2018-11-21 17:01:37', '2018-11-21 16:01:37', '[su_slider source="media: 968,966,1015,958,971" width="1600" height="600" title="no" mousewheel="no"] The
Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
As a joint research team (CNRS, Inria, Inserm, UPMC), The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
 
Team retreat at the Villa Finaly, Florence, Italy
Team retreat at the villa Finaly, Florence, Italy
', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-21 17:01:37', '2018-11-21 16:01:37', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1463, 1, '2018-11-21 17:01:56', '2018-11-21 16:01:56', '[su_slider source="media: 968,966,1015,958,971" width="1600" height="600" title="no" mousewheel="no"]
The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
As a joint research team (CNRS, Inria, Inserm, UPMC), The pluridisciplinary team is located at the Brain and Spine Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe and has a long tradition of neuroscience and neurology. This ecosystem, along with enduring collaborations with clinical teams of the ICM and the hospital, allows to develop new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models will allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical techniques empowered by computers that learn regularities in data to better alleviate similarities or differences [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Graph analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
 
Team retreat at the Villa Finaly, Florence, Italy
Team retreat at the villa Finaly, Florence, Italy
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The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
It is a joint research team between CNRS, Inria, Inserm and Sorbonne University) and belong to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical learning, Deep learning, Generative models, Bayesian models [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Magnetic resonance Imaging (MRI), Positron Emission Tomography (PET)[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Riemannian Geometry, diffeomorphisms[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Graph analysis, Multilayer networks, Network dynamics[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Non-linear mixed effects models, Riemannian geometry, Longitudinal shape models[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Neurodegenerative disease affecting primarly the cognitive capabilities of more than 30 million people over the world[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Hidden content[/su_spoiler] [/su_accordion]
 
Team retreat at the Villa Finaly, Florence, Italy
Team retreat at the villa Finaly, Florence, Italy
', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-21 17:13:54', '2018-11-21 16:13:54', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1465, 1, '2018-11-21 17:19:28', '2018-11-21 16:19:28', '[su_slider source="media: 968,966,1015,958,971" width="1600" height="600" title="no" mousewheel="no"]
The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
It is a joint research team between CNRS, Inria, Inserm and Sorbonne University) and belong to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical learning, Deep learning, Generative models, Bayesian models [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Magnetic resonance Imaging (MRI), Positron Emission Tomography (PET)[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Riemannian Geometry, diffeomorphisms[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Graph analysis, Multilayer networks, Network dynamics[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Non-linear mixed effects models, Riemannian geometry, Longitudinal shape models[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Clinical decision support systems, Disease progression models, Network alterations[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Modeling the presymptomatic phase in genetic forms of the disease[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Using multimodal neuroimaging to track disease progression[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Modeling disease progression, Computer-aided adjustment of treatment[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Using BCI for rehabilitation, BCI systems based on network analysis[/su_spoiler] [/su_accordion]
 
Team retreat at the Villa Finaly, Florence, Italy
Team retreat at the villa Finaly, Florence, Italy
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The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
It is a joint research team between CNRS, Inria, Inserm and Sorbonne University) and belong to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical learning, Deep learning, Generative models, Bayesian models [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Magnetic resonance Imaging (MRI), Positron Emission Tomography (PET)[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Riemannian Geometry, diffeomorphisms[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Graph analysis, Multilayer networks, Network dynamics[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Non-linear mixed effects models, Riemannian geometry, Longitudinal shape models[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Clinical decision support systems, Disease progression models, Network alterations[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Modeling the presymptomatic phase in genetic forms of the disease[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Using multimodal neuroimaging to track disease progression[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Modeling disease progression, Computer-aided adjustment of treatment[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Using BCI for rehabilitation, BCI systems based on network analysis[/su_spoiler] [/su_accordion]
 
Team retreat at the villa Finaly, Florence, Italy
', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-21 17:20:07', '2018-11-21 16:20:07', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1467, 1, '2018-11-21 17:21:29', '2018-11-21 16:21:29', '[su_slider source="media: 968,966,1015,958,971" width="1600" height="600" title="no" mousewheel="no"]
The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University) and belongs to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical learning, Deep learning, Generative models, Bayesian models [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Magnetic resonance Imaging (MRI), Positron Emission Tomography (PET)[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Riemannian Geometry, diffeomorphisms[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Graph analysis, Multilayer networks, Network dynamics[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Non-linear mixed effects models, Riemannian geometry, Longitudinal shape models[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Clinical decision support systems, Disease progression models, Network alterations[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Modeling the presymptomatic phase in genetic forms of the disease[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Using multimodal neuroimaging to track disease progression[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Modeling disease progression, Computer-aided adjustment of treatment[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Using BCI for rehabilitation, BCI systems based on network analysis[/su_spoiler] [/su_accordion]
 
Team retreat at the villa Finaly, Florence, Italy
 ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-21 17:21:29', '2018-11-21 16:21:29', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1468, 1, '2018-11-21 17:24:52', '2018-11-21 16:24:52', '

Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.

References


[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).

References


  • Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018. Paper
  • A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017. Paper
  • Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014. Paper
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.

References


[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

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Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.

References


[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).

References


  • Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018. Paper
  • A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017. Paper
  • Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014. Paper
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.

References


[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

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Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
References  
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
References  
  • Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018. Paper
  • A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017. Paper
  • Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014. Paper
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
References  
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Fundings

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Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
References  
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
References  
  • Network neuroscience for optimizing brain-computer interfaces. De Vico Fallani et al. 2018. Paper
  • A Topological Criterion for Filtering Information in Complex Brain Networks. De Vico Fallani et al. 2017. Paper
  • Graph analysis of functional brain networks: practical issues in translational neuroscience. De Vico Fallani. 2014. Paper
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
References  
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

  • European Union H2020 program, project EuroPOND
  • European Union H2020 program, project VirtualBrainCloud
  • IHU ICM, Investissements d\'avenir
  • ERC Starting Grant, project LEASP (S. Durrleman)
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project HIPLAY7
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project NetBCI
  • ANR, project PREVDEMALS
  • ICM Big Brain Theory Program (project DYNAMO, project PredictICD),
  • Inria Project Lab Program (project Neuromarkers)
  •   Fondation pour la Recherche sur Alzheimer (project HistoMRI)
  •   Abeona Foundation (project Brain@Scale)
  •   Fondation Vaincre Alzheimer
  • IDEX Sorbonne Universités, project LearnPETMR
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Faculty

[tmm name="faculty"]

Post-docs

[tmm name="post-docs"]

Engineers

[tmm name="engineers"]

PhD Students

[tmm name="phd-students"]

Associate fellows

[tmm name="collaborators"]

Former Members

  • Jeremy Guillon - Former PhD student - webpage
  • Michael Bacci - Former engineer
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow
  • Camille Chrétien - Former Master student
  • Hugo Boniface - Former Master student
  • Jungying Fang - Former Master student
  • Romain Valabrègue - Former Inserm Research Engineer - webpage - romain.valabregue@upmc.fr
  • Eric Bardinet - Former CNRS Research Engineer - webpage - eric.bardinet@upmc.fr
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage - sara.fernandez_vidal@upmc.fr
  • Vera Dinkelacker - Former associated clinician - v.dinkelacker@gmail.com
  • Thomas Estienne -  Former administrative assistant - CATI project - thomas.estienne@icm-institute.org
  • Martina Sundqvist - Former Master student
  • Thomas Jacquemont - Former Master student
  • Aurore Bussalb - Former Master student
  • Carlos Tor Diez - Former Master student
  • Ayoub Louati - Former Master student
  • Paul Jusselin -- Former Master student
  • Chabha Azouani - Former clinical research associate - CATI project - chabha.azouani@upmc.fr
  • Kelly Martineau -Former clinical research associate -CATI project – kelly.martineau@icm-institute.org
  • Sonia Djobeir -Former clinical research associate - CATI project – soniadjobeir@gmail.com
  • Hugo Dary - Former engineer - CATI project - dary.hugo@gmail.com
  • Ludovic Fillon - Former engineer - CATI project - ludovic.fillon@upmc.fr
  • Mathieu Dubois - Former engineer - CATI project - mathieu.dubois@icm-institute.org
  • Barbara Gris - Former PhD student -  gris@cmla.ens-cachan.fr
  • Géraldine Rousseau -Former PhD student - geraldine.rouseau@psl.ap-hop-paris.fr
  • Marika Rudler -Former PhD student - marika.rudler@psl.aphp.fr
  • Jean-Baptiste Schiratti - Former PhD student - ean-baptiste.schiratti@cmap.polytechnique.fr
  • Pietro Gori -Former PhD student - Now Assistant Professor, Telecom ParisTech
  • Ana Fouquier - -Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Alexandre Pron - Former Master student (Université Paris Descartes) - Now PhD student at INT, Marseille
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • François Deloche -Former Master student
  • Alexis Mocellin - Formet engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain -Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia -Former clinical research associate - - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Kanza Dekkiche - Former Master student
  • Antoine Latrille - Former Master student (ESIEE Paris) - Now Ingénieur Développeur Web at Easyvoyage
  • Guillaume Ruffin - Former Intern
  • Evgeny Zuenko - Former master\'s student
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Maxime Corduant - Master\'s student
  • Kevin Roussel - Master\'s student
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP - claude.adam@psl.aphp.fr
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP - sophie.dupont@psl.aphp.fr
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP - damien.galanaud@icm-institute.org
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP - yves.samson@psl.aphp.fr
  • Lionel Thivard - Neurologist (PH), AP-HP - lionel.thivard@psl.aphp.fr
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Networks

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]  
  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon et al, Scientific Reports

Longitudinal

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti et al, JMLR 2018
  • Koval et al, Front Neurosci, 2018

Morphometry

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis et al, SIAM
  • Bône et al, CVPR 2018

Machine Learning

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper Gonzalez, Neuroimage
  • Ansart et al, 2017
  • Wei, et al, MICCAI 2018

Clinical studies

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Bertrand et al, JAMA Neurol, 2018
      • Dubois et al, Lancet Neurol, 2018
      • Wen et al, JNNP, 2018
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-21 18:13:41', '2018-11-21 17:13:41', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1474, 1, '2018-11-21 18:15:53', '2018-11-21 17:15:53', '

Clinica

  References
  • Routier et al, OHBM 2018
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Leasp

To complete References
  • To complete
[su_button url="https://gitlab.icm-institute.org/aramislab/leasp" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(at)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-autosave-v1', '', '', '2018-11-21 18:15:53', '2018-11-21 17:15:53', '', 620, 'https://www.aramislab.fr/620-autosave-v1/', 0, 'revision', '', (1475, 1, '2018-11-21 18:15:54', '2018-11-21 17:15:54', '

Clinica

Clinica
  References
  • Routier et al, OHBM 2018
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Leasp

To complete References
  • To complete
[su_button url="https://gitlab.icm-institute.org/aramislab/leasp" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(at)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-21 18:15:54', '2018-11-21 17:15:54', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1476, 1, '2018-11-21 18:18:11', '2018-11-21 17:18:11', '
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).

Postdocs / Scientists

PhD thesis

Engineers / Software developers

', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2018-11-21 18:18:11', '2018-11-21 17:18:11', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '', (1477, 1, '2018-11-21 18:19:32', '2018-11-21 17:19:32', '
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).

Postdocs / Scientists

PhD thesis

Engineers / Software developers

', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2018-11-21 18:19:32', '2018-11-21 17:19:32', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '', (1478, 7, '2018-11-22 10:44:28', '2018-11-22 09:44:28', '

Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
References [su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

  • European Union H2020 program, project EuroPOND
  • European Union H2020 program, project VirtualBrainCloud
  • IHU ICM, Investissements d\'avenir
  • ERC Starting Grant, project LEASP (S. Durrleman)
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project HIPLAY7
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project NetBCI
  • ANR, project PREVDEMALS
  • ICM Big Brain Theory Program (project DYNAMO, project PredictICD),
  • Inria Project Lab Program (project Neuromarkers)
  •   Fondation pour la Recherche sur Alzheimer (project HistoMRI)
  •   Abeona Foundation (project Brain@Scale)
  •   Fondation Vaincre Alzheimer
  • IDEX Sorbonne Universités, project LearnPETMR
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-22 10:44:28', '2018-11-22 09:44:28', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1479, 7, '2018-11-22 10:44:40', '2018-11-22 09:44:40', '

Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

  • European Union H2020 program, project EuroPOND
  • European Union H2020 program, project VirtualBrainCloud
  • IHU ICM, Investissements d\'avenir
  • ERC Starting Grant, project LEASP (S. Durrleman)
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project HIPLAY7
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project NetBCI
  • ANR, project PREVDEMALS
  • ICM Big Brain Theory Program (project DYNAMO, project PredictICD),
  • Inria Project Lab Program (project Neuromarkers)
  •   Fondation pour la Recherche sur Alzheimer (project HistoMRI)
  •   Abeona Foundation (project Brain@Scale)
  •   Fondation Vaincre Alzheimer
  • IDEX Sorbonne Universités, project LearnPETMR
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-22 10:44:40', '2018-11-22 09:44:40', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1480, 7, '2018-11-22 10:45:25', '2018-11-22 09:45:25', '

Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

  • European Union H2020 program, project EuroPOND
  • European Union H2020 program, project VirtualBrainCloud
  • IHU ICM, Investissements d\'avenir
  • ERC Starting Grant, project LEASP (S. Durrleman)
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project HIPLAY7
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project NetBCI
  • ANR, project PREVDEMALS
  • ICM Big Brain Theory Program (project DYNAMO, project PredictICD),
  • Inria Project Lab Program (project Neuromarkers)
  • Fondation pour la Recherche sur Alzheimer (project HistoMRI)
  • Abeona Foundation (project Brain@Scale)
  • Fondation Vaincre Alzheimer
  • IDEX Sorbonne Universités, project LearnPETMR
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-22 10:45:25', '2018-11-22 09:45:25', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1481, 7, '2018-11-22 10:49:20', '2018-11-22 09:49:20', '

Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

  • European Union H2020 program, project EuroPOND
  • European Union H2020 program, project VirtualBrainCloud
  • IHU ICM, Investissements d\'avenir
  • ERC Starting Grant, project LEASP (S. Durrleman)
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project HIPLAY7
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project NetBCI
  • ANR, project PREVDEMALS
  • ICM Big Brain Theory Program (project DYNAMO, project PredictICD),
  • Inria Project Lab Program (project Neuromarkers)
  • Fondation pour la Recherche sur Alzheimer (project HistoMRI)
  • Abeona Foundation (project Brain@Scale)
  • Fondation Vaincre Alzheimer
  • IDEX Sorbonne Universités, project LearnPETMR
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-22 10:49:20', '2018-11-22 09:49:20', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1482, 7, '2018-11-22 10:54:38', '2018-11-22 09:54:38', '

Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

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Funding / main grants

  • European Union H2020 program, project EuroPOND
  • European Union H2020 program, project VirtualBrainCloud
  • IHU ICM, Investissements d\'avenir
  • ERC Starting Grant, project LEASP (S. Durrleman)
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project HIPLAY7
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project NetBCI
  • ANR, project PREVDEMALS
  • ICM Big Brain Theory Program (project DYNAMO, project PredictICD),
  • Inria Project Lab Program (project Neuromarkers)
  • Fondation pour la Recherche sur Alzheimer (project HistoMRI)
  • Abeona Foundation (project Brain@Scale)
  • Fondation Vaincre Alzheimer
  • IDEX Sorbonne Universités, project LearnPETMR
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-22 10:54:38', '2018-11-22 09:54:38', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1483, 7, '2018-11-22 10:56:02', '2018-11-22 09:56:02', '

Most representative publications

Networks
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]  
  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon et al, Scientific Reports

Longitudinal

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti et al, JMLR 2018
  • Koval et al, Front Neurosci, 2018

Morphometry

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis et al, SIAM
  • Bône et al, CVPR 2018

Machine Learning

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper Gonzalez, Neuroimage
  • Ansart et al, 2017
  • Wei, et al, MICCAI 2018

Clinical studies

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Bertrand et al, JAMA Neurol, 2018
      • Dubois et al, Lancet Neurol, 2018
      • Wen et al, JNNP, 2018
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 10:56:02', '2018-11-22 09:56:02', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1484, 7, '2018-11-22 10:56:58', '2018-11-22 09:56:58', '

Most representative publications

Networks

 
  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon et al, Scientific Reports

Longitudinal

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti et al, JMLR 2018
  • Koval et al, Front Neurosci, 2018
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis et al, SIAM
  • Bône et al, CVPR 2018
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper Gonzalez, Neuroimage
  • Ansart et al, 2017
  • Wei, et al, MICCAI 2018

Clinical studies

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Bertrand et al, JAMA Neurol, 2018
      • Dubois et al, Lancet Neurol, 2018
      • Wen et al, JNNP, 2018
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 10:56:58', '2018-11-22 09:56:58', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1485, 7, '2018-11-22 10:57:13', '2018-11-22 09:57:13', '

Most representative publications

Networks

 
  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon et al, Scientific Reports
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti et al, JMLR 2018
  • Koval et al, Front Neurosci, 2018
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis et al, SIAM
  • Bône et al, CVPR 2018
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper Gonzalez, Neuroimage
  • Ansart et al, 2017
  • Wei, et al, MICCAI 2018

Clinical studies

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]
      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Bertrand et al, JAMA Neurol, 2018
      • Dubois et al, Lancet Neurol, 2018
      • Wen et al, JNNP, 2018
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 10:57:13', '2018-11-22 09:57:13', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1486, 7, '2018-11-22 10:57:36', '2018-11-22 09:57:36', '

Most representative publications

Networks

 
  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon et al, Scientific Reports
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti et al, JMLR 2018
  • Koval et al, Front Neurosci, 2018
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis et al, SIAM
  • Bône et al, CVPR 2018
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper Gonzalez, Neuroimage
  • Ansart et al, 2017
  • Wei, et al, MICCAI 2018
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinical studies

      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Bertrand et al, JAMA Neurol, 2018
      • Dubois et al, Lancet Neurol, 2018
      • Wen et al, JNNP, 2018
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 10:57:36', '2018-11-22 09:57:36', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1487, 7, '2018-11-22 17:30:32', '2018-11-22 16:30:32', '

Faculty

[tmm name="faculty"]

Post-docs

[tmm name="post-docs"]

Engineers

[tmm name="engineers"]

PhD Students

[tmm name="phd-students"]

Associate fellows

[tmm name="collaborators"]

Former Members

  • Jeremy Guillon - Former PhD student - webpage
  • Michael Bacci - Former engineer
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow
  • Romain Valabrègue - Former Inserm Research Engineer - webpage - romain.valabregue@upmc.fr
  • Eric Bardinet - Former CNRS Research Engineer - webpage - eric.bardinet@upmc.fr
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage - sara.fernandez_vidal@upmc.fr
  • Vera Dinkelacker - Former associated clinician - v.dinkelacker@gmail.com
  • Thomas Estienne -  Former administrative assistant - CATI project - thomas.estienne@icm-institute.org
  • Chabha Azouani - Former clinical research associate - CATI project - chabha.azouani@upmc.fr
  • Kelly Martineau -Former clinical research associate -CATI project – kelly.martineau@icm-institute.org
  • Sonia Djobeir -Former clinical research associate - CATI project – soniadjobeir@gmail.com
  • Hugo Dary - Former engineer - CATI project - dary.hugo@gmail.com
  • Ludovic Fillon - Former engineer - CATI project - ludovic.fillon@upmc.fr
  • Mathieu Dubois - Former engineer - CATI project - mathieu.dubois@icm-institute.org
  • Barbara Gris - Former PhD student -  gris@cmla.ens-cachan.fr
  • Géraldine Rousseau -Former PhD student - geraldine.rouseau@psl.ap-hop-paris.fr
  • Marika Rudler -Former PhD student - marika.rudler@psl.aphp.fr
  • Jean-Baptiste Schiratti - Former PhD student - ean-baptiste.schiratti@cmap.polytechnique.fr
  • Pietro Gori -Former PhD student - Now Assistant Professor, Telecom ParisTech
  • Ana Fouquier - -Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Formet engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain -Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia -Former clinical research associate - - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Guillaume Ruffin - Former Intern
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP - claude.adam@psl.aphp.fr
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP - sophie.dupont@psl.aphp.fr
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP - damien.galanaud@icm-institute.org
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP - yves.samson@psl.aphp.fr
  • Lionel Thivard - Neurologist (PH), AP-HP - lionel.thivard@psl.aphp.fr
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2018-11-22 17:30:32', '2018-11-22 16:30:32', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '', (1488, 7, '2018-11-22 17:34:39', '2018-11-22 16:34:39', '

Faculty

[tmm name="faculty"]

Post-docs

[tmm name="post-docs"]

Engineers

[tmm name="engineers"]

PhD Students

[tmm name="phd-students"]

Associate fellows

[tmm name="collaborators"]

Former Members

  • Jeremy Guillon - Former PhD student - webpage
  • Michael Bacci - Former engineer
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow
  • Romain Valabrègue - Former Inserm Research Engineer - webpage - romain.valabregue@upmc.fr
  • Eric Bardinet - Former CNRS Research Engineer - webpage - eric.bardinet@upmc.fr
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage - sara.fernandez_vidal@upmc.fr
  • Vera Dinkelacker - Former associated clinician - v.dinkelacker@gmail.com
  • Thomas Estienne -  Former administrative assistant - CATI project - thomas.estienne@icm-institute.org
  • Chabha Azouani - Former clinical research associate - CATI project - chabha.azouani@upmc.fr
  • Kelly Martineau -Former clinical research associate -CATI project – kelly.martineau@icm-institute.org
  • Sonia Djobeir -Former clinical research associate - CATI project – soniadjobeir@gmail.com
  • Hugo Dary - Former engineer - CATI project - dary.hugo@gmail.com
  • Ludovic Fillon - Former engineer - CATI project - ludovic.fillon@upmc.fr
  • Mathieu Dubois - Former engineer - CATI project - mathieu.dubois@icm-institute.org
  • Barbara Gris - Former PhD student-  gris@cmla.ens-cachan.fr
  • Géraldine Rousseau -Former PhD student - geraldine.rouseau@psl.ap-hop-paris.fr
  • Marika Rudler -Former PhD student - marika.rudler@psl.aphp.fr
  • Jean-Baptiste Schiratti - Former PhD student - ean-baptiste.schiratti@cmap.polytechnique.fr
  • Pietro Gori -Former PhD student - Now Assistant Professor, Telecom ParisTech
  • Ana Fouquier - -Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Formet engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain -Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia -Former clinical research associate - - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Guillaume Ruffin - Former Intern
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP - claude.adam@psl.aphp.fr
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP - sophie.dupont@psl.aphp.fr
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP - damien.galanaud@icm-institute.org
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP - yves.samson@psl.aphp.fr
  • Lionel Thivard - Neurologist (PH), AP-HP - lionel.thivard@psl.aphp.fr
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2018-11-22 17:34:39', '2018-11-22 16:34:39', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '', (1489, 7, '2018-11-22 17:36:21', '2018-11-22 16:36:21', '

Most representative publications

Networks

 
  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon et al, Scientific Reports
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti et al, JMLR 2018
  • Koval et al, Front Neurosci, 2018
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis et al, SIAM
  • Bône et al, CVPR 2018
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper Gonzalez, Neuroimage
  • Ansart et al, 2017
  • Wei, et al, MICCAI 2018
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinical studies

      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Bertrand et al, JAMA Neurol, 2018
      • Dubois et al, Lancet Neurol, 2018
      • Wen et al, JNNP, 2018
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 17:36:21', '2018-11-22 16:36:21', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1490, 7, '2018-11-22 17:36:51', '2018-11-22 16:36:51', '

Most representative publications

Networks

 
  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon et al, Scientific Reports
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti et al, JMLR 2018
  • Koval et al, Front Neurosci, 2018
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis et al, SIAM
  • Bône et al, CVPR 2018
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper Gonzalez, Neuroimage
  • Ansart et al, 2017
  • Wei, et al, MICCAI 2018
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinical studies

      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Bertrand et al, JAMA Neurol, 2018
      • Dubois et al, Lancet Neurol, 2018
      • Wen et al, JNNP, 2018
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 17:36:51', '2018-11-22 16:36:51', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1491, 7, '2018-11-22 17:46:01', '2018-11-22 16:46:01', '

Most representative publications

Networks

 
  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease Scientific Reports 7;10879. 2017.PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti et al, JMLR 2018
  • Koval et al, Front Neurosci, 2018
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis et al, SIAM
  • Bône et al, CVPR 2018
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper Gonzalez, Neuroimage
  • Ansart et al, 2017
  • Wei, et al, MICCAI 2018
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinical studies

      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Bertrand et al, JAMA Neurol, 2018
      • Dubois et al, Lancet Neurol, 2018
      • Wen et al, JNNP, 2018
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 17:46:01', '2018-11-22 16:46:01', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1492, 7, '2018-11-22 17:51:45', '2018-11-22 16:51:45', '

Most representative publications

Networks

 
  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017.PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • . In ..., 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis et al, SIAM
  • Bône et al, CVPR 2018
  • . In ..., 2018. PDF Paper in PDF
  • . In ..., 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper Gonzalez, Neuroimage
  • Ansart et al, 2017
  • Wei, et al, MICCAI 2018
  • . In ..., 2018. PDF Paper in PDF
  • . In ..., 2018. PDF Paper in PDF
  • . In ..., 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinical studies

      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • . In ..., 2018. PDF Paper in PDF
      • . In ..., 2018. PDF Paper in PDF
      • . In ..., 2018. PDF Paper in PDF
      • . In ..., 2018. PDF Paper in PDF
      • Bertrand et al, JAMA Neurol, 2018
      • Dubois et al, Lancet Neurol, 2018
      • Wen et al, JNNP, 2018
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 17:51:45', '2018-11-22 16:51:45', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1493, 7, '2018-11-22 17:57:09', '2018-11-22 16:57:09', '

Most representative publications

Networks

 
  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017.PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis et al. In SIAM ..., 2017. PDF Paper in PDF
  • Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper Gonzalez, Neuroimage
  • Ansart et al, 2017
  • Wei, et al, MICCAI 2018
  • . In ..., 2018. PDF Paper in PDF
  • . In ..., 2018. PDF Paper in PDF
  • . In ..., 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinical studies

      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • . In ..., 2018. PDF Paper in PDF
      • . In ..., 2018. PDF Paper in PDF
      • . In ..., 2018. PDF Paper in PDF
      • . In ..., 2018. PDF Paper in PDF
      • Bertrand et al, JAMA Neurol, 2018
      • Dubois et al, Lancet Neurol, 2018
      • Wen et al, JNNP, 2018
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 17:57:09', '2018-11-22 16:57:09', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1494, 7, '2018-11-22 18:01:17', '2018-11-22 17:01:17', '

Most representative publications

Networks

 
  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017.PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis et al. In SIAM ..., 2017. PDF Paper in PDF
  • Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper Gonzalez. In Neuroimage ..., 2018. PDF Paper in PDF
  • Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 357-364. 2017. PDF Paper in PDF
  • Wei et al. . In MICCAI. 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinical studies

      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • . In ..., 2018. PDF Paper in PDF
      • . In ..., 2018. PDF Paper in PDF
      • . In ..., 2018. PDF Paper in PDF
      • . In ..., 2018. PDF Paper in PDF
      • Bertrand et al, JAMA Neurol, 2018
      • Dubois et al, Lancet Neurol, 2018
      • Wen et al, JNNP, 2018
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 18:01:17', '2018-11-22 17:01:17', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1495, 7, '2018-11-22 18:09:17', '2018-11-22 17:09:17', '

Most representative publications

Networks

  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017.PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis et al. In SIAM ..., 2017. PDF Paper in PDF
  • Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper Gonzalez. In Neuroimage ..., 2018. PDF Paper in PDF
  • Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 357-364. 2017. PDF Paper in PDF
  • Wei et al. . In MICCAI. 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinical studies

      • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
      • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
      • Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. PDF Paper in PDF
      • Dubois et al. . In Lancet in Neurology. ..., 2018. PDF Paper in PDF
      • Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 18:09:17', '2018-11-22 17:09:17', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1496, 7, '2018-11-22 18:09:34', '2018-11-22 17:09:34', '

Most representative publications

Networks

  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017.PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis et al. In SIAM ..., 2017. PDF Paper in PDF
  • Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper Gonzalez. In Neuroimage ..., 2018. PDF Paper in PDF
  • Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 357-364. 2017. PDF Paper in PDF
  • Wei et al. . In MICCAI. 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinical studies

  • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
  • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
  • Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. PDF Paper in PDF
  • Dubois et al. . In Lancet in Neurology. ..., 2018. PDF Paper in PDF
  • Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-11-22 18:09:34', '2018-11-22 17:09:34', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1497, 7, '2018-11-22 18:14:49', '2018-11-22 17:14:49', '

Clinica

Clinica
  References
  • Routier A, Habert MO, Bertrand A, Kas A, David PM, Bertin H, Godefroy O, Etcharry-Bouyx F, Moreaud O, Pasquier F, Couratier P, Bennys K, Coutoleau Bretoniere C, Martinaud O, Laurent B, Pariente J Puel M, Belliard S, Migliaccio R, Dubois B, Colliot O, Teichmann M. Structural, microstructural and metabolic alterations in Primary Progressive Aphasia variants. In OHBM 2018. PDF Paper in PDF
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them. References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. References
  • To complete
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Leasp

To complete References
  • To complete
[su_button url="https://gitlab.icm-institute.org/aramislab/leasp" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(at)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-11-22 18:14:49', '2018-11-22 17:14:49', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1498, 7, '2018-11-22 18:23:08', '2018-11-22 17:23:08', '', 'Capture d’écran 2018-11-22 à 18.24.05', '', 'inherit', 'closed', 'closed', '', 'capture-decran-2018-11-22-a-18-24-05', '', '', '2018-11-22 18:23:08', '2018-11-22 17:23:08', '', 22, 'https://www.aramislab.fr/wp-content/uploads/2018/11/Capture-d’écran-2018-11-22-à-18.24.05.png', 0, 'attachment', 'image/png', (1499, 7, '2018-11-22 18:23:08', '2018-11-22 17:23:08', '', 'Capture d’écran 2018-11-22 à 18.24.11', '', 'inherit', 'closed', 'closed', '', 'capture-decran-2018-11-22-a-18-24-11', '', '', '2018-11-22 18:23:08', '2018-11-22 17:23:08', '', 22, 'https://www.aramislab.fr/wp-content/uploads/2018/11/Capture-d’écran-2018-11-22-à-18.24.11.png', 0, 'attachment', 'image/png', (1500, 7, '2018-11-22 18:23:09', '2018-11-22 17:23:09', '', 'Capture d’écran 2018-11-22 à 18.26.12', '', 'inherit', 'closed', 'closed', '', 'capture-decran-2018-11-22-a-18-26-12', '', '', '2018-11-22 18:23:09', '2018-11-22 17:23:09', '', 22, 'https://www.aramislab.fr/wp-content/uploads/2018/11/Capture-d’écran-2018-11-22-à-18.26.12.png', 0, 'attachment', 'image/png', (1501, 7, '2018-11-22 18:23:10', '2018-11-22 17:23:10', '', 'Capture d’écran 2018-11-22 à 18.26.58', '', 'inherit', 'closed', 'closed', '', 'capture-decran-2018-11-22-a-18-26-58', '', '', '2018-11-22 18:23:10', '2018-11-22 17:23:10', '', 22, 'https://www.aramislab.fr/wp-content/uploads/2018/11/Capture-d’écran-2018-11-22-à-18.26.58.png', 0, 'attachment', 'image/png', (1502, 7, '2018-11-22 18:23:11', '2018-11-22 17:23:11', '', 'Capture d’écran 2018-11-22 à 18.28.03', '', 'inherit', 'closed', 'closed', '', 'capture-decran-2018-11-22-a-18-28-03', '', '', '2018-11-22 18:23:11', '2018-11-22 17:23:11', '', 22, 'https://www.aramislab.fr/wp-content/uploads/2018/11/Capture-d’écran-2018-11-22-à-18.28.03.png', 0, 'attachment', 'image/png', (1503, 7, '2018-11-22 18:26:07', '2018-11-22 17:26:07', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from m

ultimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to int

egrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framew ork for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

   

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

  • European Union H2020 program, project EuroPOND
  • European Union H2020 program, project VirtualBrainCloud
  • IHU ICM, Investissements d\'avenir
  • ERC Starting Grant, project LEASP (S. Durrleman)
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project HIPLAY7
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project NetBCI
  • ANR, project PREVDEMALS
  • ICM Big Brain Theory Program (project DYNAMO, project PredictICD),
  • Inria Project Lab Program (project Neuromarkers)
  • Fondation pour la Recherche sur Alzheimer (project HistoMRI)
  • Abeona Foundation (project Brain@Scale)
  • Fondation Vaincre Alzheimer
  • IDEX Sorbonne Universités, project LearnPETMR
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-22 18:26:07', '2018-11-22 17:26:07', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', 0); INSERT INTO `wp_aramis_posts` VALUES (1504, 7, '2018-11-22 18:26:28', '2018-11-22 17:26:28', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framew ork for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

   

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

  • European Union H2020 program, project EuroPOND
  • European Union H2020 program, project VirtualBrainCloud
  • IHU ICM, Investissements d\'avenir
  • ERC Starting Grant, project LEASP (S. Durrleman)
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project HIPLAY7
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project NetBCI
  • ANR, project PREVDEMALS
  • ICM Big Brain Theory Program (project DYNAMO, project PredictICD),
  • Inria Project Lab Program (project Neuromarkers)
  • Fondation pour la Recherche sur Alzheimer (project HistoMRI)
  • Abeona Foundation (project Brain@Scale)
  • Fondation Vaincre Alzheimer
  • IDEX Sorbonne Universités, project LearnPETMR
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-22 18:26:28', '2018-11-22 17:26:28', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1505, 7, '2018-11-22 18:27:02', '2018-11-22 17:27:02', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framew ork for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

  • European Union H2020 program, project EuroPOND
  • European Union H2020 program, project VirtualBrainCloud
  • IHU ICM, Investissements d\'avenir
  • ERC Starting Grant, project LEASP (S. Durrleman)
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project HIPLAY7
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project NetBCI
  • ANR, project PREVDEMALS
  • ICM Big Brain Theory Program (project DYNAMO, project PredictICD),
  • Inria Project Lab Program (project Neuromarkers)
  • Fondation pour la Recherche sur Alzheimer (project HistoMRI)
  • Abeona Foundation (project Brain@Scale)
  • Fondation Vaincre Alzheimer
  • IDEX Sorbonne Universités, project LearnPETMR
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-22 18:27:02', '2018-11-22 17:27:02', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1506, 1, '2018-11-22 20:13:29', '2018-11-22 19:13:29', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framew ork for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

  • European Union H2020 program, project EuroPOND
  • European Union H2020 program, project VirtualBrainCloud
  • IHU ICM, Investissements d\'avenir
  • ERC Starting Grant, project LEASP (S. Durrleman)
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project HIPLAY7
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project NetBCI
  • ANR, project PREVDEMALS
  • ICM Big Brain Theory Program (project DYNAMO, project PredictICD),
  • Inria Project Lab Program (project Neuromarkers)
  • Fondation pour la Recherche sur Alzheimer (project HistoMRI)
  • Abeona Foundation (project Brain@Scale)
  • Fondation Vaincre Alzheimer
  • IDEX Sorbonne Universités, project LearnPETMR
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-22 20:13:29', '2018-11-22 19:13:29', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1507, 1, '2018-11-22 20:15:37', '2018-11-22 19:15:37', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framew ork for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

  • European Union H2020 program, project EuroPOND
  • European Union H2020 program, project VirtualBrainCloud
  • IHU ICM, Investissements d\'avenir
  • ERC Starting Grant, project LEASP (S. Durrleman)
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project HIPLAY7
  • NSF/NIH/ANR program “Collaborative Research in Computational Neuroscience”, project NetBCI
  • ANR, project PREVDEMALS
  • ICM Big Brain Theory Program (project DYNAMO, project PredictICD),
  • Inria Project Lab Program (project Neuromarkers)
  • Fondation pour la Recherche sur Alzheimer (project HistoMRI)
  • Abeona Foundation (project Brain@Scale)
  • Fondation Vaincre Alzheimer
  • IDEX Sorbonne Universités, project LearnPETMR
', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-22 20:15:37', '2018-11-22 19:15:37', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1508, 1, '2018-11-22 20:25:06', '2018-11-22 19:25:06', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framew ork for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-22 20:25:06', '2018-11-22 19:25:06', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1509, 1, '2018-11-22 20:27:26', '2018-11-22 19:27:26', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framew ork for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-22 20:27:26', '2018-11-22 19:27:26', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1510, 1, '2018-11-22 20:34:55', '2018-11-22 19:34:55', '[su_slider source="media: 968,966,1015,958,971" width="1600" height="600" title="no" mousewheel="no"]
The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University) and belongs to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
We are financially supported by these institutions.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical learning, Deep learning, Generative models, Bayesian models [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Magnetic resonance Imaging (MRI), Positron Emission Tomography (PET)[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Riemannian Geometry, diffeomorphisms[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Graph analysis, Multilayer networks, Network dynamics[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Non-linear mixed effects models, Riemannian geometry, Longitudinal shape models[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Clinical decision support systems, Disease progression models, Network alterations[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Modeling the presymptomatic phase in genetic forms of the disease[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Using multimodal neuroimaging to track disease progression[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Modeling disease progression, Computer-aided adjustment of treatment[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Using BCI for rehabilitation, BCI systems based on network analysis[/su_spoiler] [/su_accordion]
 
Team retreat at the villa Finaly, Florence, Italy
 ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-22 20:34:55', '2018-11-22 19:34:55', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1511, 1, '2018-11-22 20:35:30', '2018-11-22 19:35:30', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framew ork for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-22 20:35:30', '2018-11-22 19:35:30', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1512, 1, '2018-11-23 15:40:07', '2018-11-23 14:40:07', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framew ork for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-23 15:40:07', '2018-11-23 14:40:07', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1513, 7, '2018-11-26 17:36:25', '2018-11-26 16:36:25', '[su_slider source="media: 968,966,1015,958,971" width="1600" height="600" title="no" mousewheel="no"]
The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University) and belongs to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
We are financially supported by these institutions.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical learning, Deep learning, Generative models, Bayesian models [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Magnetic resonance Imaging (MRI), Positron Emission Tomography (PET)[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Riemannian Geometry, diffeomorphisms[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Graph analysis, Multilayer networks, Network dynamics[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Non-linear mixed effects models, Riemannian geometry, Longitudinal shape models[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Clinical decision support systems, Disease progression models, Network alterations[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Modeling the presymptomatic phase in genetic forms of the disease[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Using multimodal neuroimaging to track disease progression[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Modeling disease progression, Computer-aided adjustment of treatment[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Using BCI for rehabilitation, BCI systems based on network analysis[/su_spoiler] [/su_accordion]
 
Team retreat at the villa Finaly, Florence, Italy
 ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-26 17:36:25', '2018-11-26 16:36:25', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1514, 7, '2018-11-26 17:36:43', '2018-11-26 16:36:43', '[su_slider source="media: 968,966,1015,958,971" width="1600" height="600" title="no" mousewheel="no"]
The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numedical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University) and belongs to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
We are financially supported by these institutions.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical learning, Deep learning, Generative models, Bayesian models [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Magnetic resonance Imaging (MRI), Positron Emission Tomography (PET)[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Riemannian Geometry, diffeomorphisms[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Graph analysis, Multilayer networks, Network dynamics[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Non-linear mixed effects models, Riemannian geometry, Longitudinal shape models[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Clinical decision support systems, Disease progression models, Network alterations[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Modeling the presymptomatic phase in genetic forms of the disease[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Using multimodal neuroimaging to track disease progression[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Modeling disease progression, Computer-aided adjustment of treatment[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Using BCI for rehabilitation, BCI systems based on network analysis[/su_spoiler] [/su_accordion]
 
Team retreat at the villa Finaly, Florence, Italy
 ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2018-11-26 17:36:43', '2018-11-26 16:36:43', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1515, 7, '2018-11-26 17:38:45', '2018-11-26 16:38:45', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

The complexity of biological systems often emerges from interactions between components at multiple spatial and temporal scales. Neglecting this information, and analyzing separately the levels of such scales, is an oversimplification of the real phenomenon. We propose a methodological framework that aims, on the one hand, to integrate information from networks describing different modes of connectivity (e.g. multi-modal, cross-frequency) and, on the other hand, to statistically model the organizational mechanisms of temporally dynamic networks (e.g. nonstationary, longitudinal). Target applications include: i) human learning in brain-computer interface, ii) prediction of neurodegenerative disease progression, and iii) identification of driving nodes in biological pathways (gene expression networks).
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-26 17:38:45', '2018-11-26 16:38:45', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1516, 7, '2018-11-26 17:39:16', '2018-11-26 16:39:16', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-26 17:39:16', '2018-11-26 16:39:16', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1517, 7, '2018-11-26 17:40:24', '2018-11-26 16:40:24', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-26 17:40:24', '2018-11-26 16:40:24', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1518, 7, '2018-11-26 17:43:27', '2018-11-26 16:43:27', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-26 17:43:27', '2018-11-26 16:43:27', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1519, 7, '2018-11-26 17:44:21', '2018-11-26 16:44:21', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-26 17:44:21', '2018-11-26 16:44:21', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1520, 7, '2018-11-26 17:45:21', '2018-11-26 16:45:21', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-26 17:45:21', '2018-11-26 16:45:21', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1521, 7, '2018-11-26 17:50:33', '2018-11-26 16:50:33', '

Faculty

[tmm name="faculty"]

Post-docs

[tmm name="post-docs"]

Engineers

[tmm name="engineers"]

PhD Students

[tmm name="phd-students"]

Associate fellows

[tmm name="collaborators"]

Former Members

  • Jeremy Guillon - Former PhD student - webpage
  • Michael Bacci - Former engineer
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow
  • Romain Valabrègue - Former Inserm Research Engineer
  • Eric Bardinet - Former CNRS Research Engineer - webpage
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage
  • Vera Dinkelacker - Former associated clinician
  • Thomas Estienne -  Former administrative assistant - CATI project
  • Chabha Azouani - Former clinical research associate - CATI project
  • Kelly Martineau -Former clinical research associate -CATI project
  • Sonia Djobeir -Former clinical research associate - CATI project
  • Hugo Dary - Former engineer - CATI project
  • Ludovic Fillon - Former engineer - CATI project
  • Mathieu Dubois - Former engineer - CATI project
  • Barbara Gris - Former PhD student
  • Géraldine Rousseau -Former PhD student
  • Marika Rudler -Former PhD student
  • Jean-Baptiste Schiratti - Former PhD student
  • Pietro Gori -Former PhD student - Now Assistant Professor, Telecom ParisTech
  • Ana Fouquier - -Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Formet engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain -Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia -Former clinical research associate - - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Guillaume Ruffin - Former Intern
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2018-11-26 17:50:33', '2018-11-26 16:50:33', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '', (1522, 7, '2018-11-26 17:51:18', '2018-11-26 16:51:18', '

Faculty

[tmm name="faculty"]

Post-docs

[tmm name="post-docs"]

Engineers

[tmm name="engineers"]

PhD Students

[tmm name="phd-students"]

Associate fellows

[tmm name="collaborators"]

Former Members

  • Jeremy Guillon - Former PhD student - webpage
  • Michael Bacci - Former engineer
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow
  • Romain Valabrègue - Former Inserm Research Engineer
  • Eric Bardinet - Former CNRS Research Engineer - webpage
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage
  • Vera Dinkelacker - Former associated clinician
  • Thomas Estienne -  Former administrative assistant - CATI project
  • Chabha Azouani - Former clinical research associate - CATI project
  • Kelly Martineau -Former clinical research associate -CATI project
  • Sonia Djobeir -Former clinical research associate - CATI project
  • Hugo Dary - Former engineer - CATI project
  • Ludovic Fillon - Former engineer - CATI project
  • Mathieu Dubois - Former engineer - CATI project
  • Barbara Gris - Former PhD student
  • Géraldine Rousseau -Former PhD student
  • Marika Rudler -Former PhD student
  • Jean-Baptiste Schiratti - Former PhD student
  • Pietro Gori -Former PhD student - Now Assistant Professor, Telecom ParisTech
  • Ana Fouquier - -Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Formet engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain -Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia -Former clinical research associate - - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2018-11-26 17:51:18', '2018-11-26 16:51:18', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '', (1524, 7, '2018-11-26 18:01:04', '2018-11-26 17:01:04', '', 'plan', '', 'inherit', 'closed', 'closed', '', 'plan-2', '', '', '2018-11-26 18:01:04', '2018-11-26 17:01:04', '', 0, 'https://www.aramislab.fr/wp-content/uploads/2018/11/plan-1.png', 0, 'attachment', 'image/png', (1525, 7, '2018-11-26 18:54:43', '2018-11-26 17:54:43', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-26 18:54:43', '2018-11-26 17:54:43', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1526, 7, '2018-11-26 18:55:35', '2018-11-26 17:55:35', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-26 18:55:35', '2018-11-26 17:55:35', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1527, 7, '2018-11-26 18:57:09', '2018-11-26 17:57:09', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-26 18:57:09', '2018-11-26 17:57:09', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1528, 7, '2018-11-26 18:58:26', '2018-11-26 17:58:26', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-26 18:58:26', '2018-11-26 17:58:26', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1529, 7, '2018-11-26 19:02:39', '2018-11-26 18:02:39', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-26 19:02:39', '2018-11-26 18:02:39', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1530, 7, '2018-11-26 19:06:59', '2018-11-26 18:06:59', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-26 19:06:59', '2018-11-26 18:06:59', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1531, 7, '2018-11-26 19:10:01', '2018-11-26 18:10:01', '', 'NetworkLeMonde', '', 'inherit', 'closed', 'closed', '', 'networklemonde', '', '', '2018-11-26 19:10:01', '2018-11-26 18:10:01', '', 22, 'https://www.aramislab.fr/wp-content/uploads/2018/11/NetworkLeMonde.png', 0, 'attachment', 'image/png', (1532, 7, '2018-11-26 19:10:38', '2018-11-26 18:10:38', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-26 19:10:38', '2018-11-26 18:10:38', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1533, 7, '2018-11-26 19:13:16', '2018-11-26 18:13:16', '', 'longitudinal', '', 'inherit', 'closed', 'closed', '', 'longitudinal', '', '', '2018-11-26 19:13:16', '2018-11-26 18:13:16', '', 22, 'https://www.aramislab.fr/wp-content/uploads/2018/11/longitudinal.png', 0, 'attachment', 'image/png', (1534, 7, '2018-11-26 19:13:43', '2018-11-26 18:13:43', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-26 19:13:43', '2018-11-26 18:13:43', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1535, 7, '2018-11-26 19:15:09', '2018-11-26 18:15:09', '', 'stanley_durrleman', '', 'inherit', 'closed', 'closed', '', 'stanley_durrleman', '', '', '2018-11-26 19:15:09', '2018-11-26 18:15:09', '', 1177, 'https://www.aramislab.fr/wp-content/uploads/2018/11/stanley_durrleman.jpg', 0, 'attachment', 'image/jpeg', (1536, 7, '2018-11-27 10:40:24', '2018-11-27 09:40:24', '', 'catalina_obando', '', 'inherit', 'closed', 'closed', '', 'catalina_obando', '', '', '2018-11-27 10:40:24', '2018-11-27 09:40:24', '', 1181, 'https://www.aramislab.fr/wp-content/uploads/2018/11/catalina_obando.jpg', 0, 'attachment', 'image/jpeg', (1537, 7, '2018-11-27 11:18:33', '2018-11-27 10:18:33', '', 'research_topic_network', '', 'inherit', 'closed', 'closed', '', 'research_topic_network', '', '', '2018-11-27 11:18:53', '2018-11-27 10:18:53', '', 22, 'https://www.aramislab.fr/wp-content/uploads/2018/11/research_topic_network.png', 0, 'attachment', 'image/png', (1538, 7, '2018-11-27 11:19:06', '2018-11-27 10:19:06', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-27 11:19:06', '2018-11-27 10:19:06', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1539, 7, '2018-11-27 11:19:28', '2018-11-27 10:19:28', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-27 11:19:28', '2018-11-27 10:19:28', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1541, 7, '2018-11-27 11:26:00', '2018-11-27 10:26:00', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-27 11:26:00', '2018-11-27 10:26:00', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1542, 7, '2018-11-27 11:26:34', '2018-11-27 10:26:34', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-27 11:26:34', '2018-11-27 10:26:34', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1543, 7, '2018-11-27 11:27:05', '2018-11-27 10:27:05', '', 'network_3', '', 'inherit', 'closed', 'closed', '', 'network_3', '', '', '2018-11-27 11:27:05', '2018-11-27 10:27:05', '', 22, 'https://www.aramislab.fr/wp-content/uploads/2018/11/network_3.png', 0, 'attachment', 'image/png', (1544, 7, '2018-11-27 11:27:20', '2018-11-27 10:27:20', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-27 11:27:20', '2018-11-27 10:27:20', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1545, 7, '2018-11-27 11:38:33', '2018-11-27 10:38:33', '', 'maximelouis', '', 'inherit', 'closed', 'closed', '', 'maximelouis', '', '', '2018-11-27 11:38:33', '2018-11-27 10:38:33', '', 1181, 'https://www.aramislab.fr/wp-content/uploads/2018/11/maximelouis.png', 0, 'attachment', 'image/png', (1547, 7, '2018-11-27 12:01:11', '2018-11-27 11:01:11', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-11-27 12:01:11', '2018-11-27 11:01:11', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1548, 7, '2018-11-27 12:01:30', '2018-11-27 11:01:30', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

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    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

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    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

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  • Michael Bacci - Former engineer
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow
  • Romain Valabrègue - Former Inserm Research Engineer
  • Eric Bardinet - Former CNRS Research Engineer - webpage
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage
  • Vera Dinkelacker - Former associated clinician
  • Thomas Estienne -  Former administrative assistant - CATI project
  • Chabha Azouani - Former clinical research associate - CATI project
  • Kelly Martineau -Former clinical research associate -CATI project
  • Sonia Djobeir -Former clinical research associate - CATI project
  • Hugo Dary - Former engineer - CATI project
  • Ludovic Fillon - Former engineer - CATI project
  • Mathieu Dubois - Former engineer - CATI project
  • Barbara Gris - Former PhD student
  • Géraldine Rousseau -Former PhD student
  • Marika Rudler -Former PhD student
  • Jean-Baptiste Schiratti - Former PhD student
  • Pietro Gori -Former PhD student - webpage
  • Ana Fouquier - -Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Formet engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain -Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia -Former clinical research associate - - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC - dominique.hasboun@upmc.fr
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP
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  • Michael Bacci - Former engineer
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow
  • Romain Valabrègue - Former Inserm Research Engineer
  • Eric Bardinet - Former CNRS Research Engineer - webpage
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage
  • Vera Dinkelacker - Former associated clinician
  • Thomas Estienne -  Former administrative assistant - CATI project
  • Chabha Azouani - Former clinical research associate - CATI project
  • Kelly Martineau - Former clinical research associate -CATI project
  • Sonia Djobeir - Former clinical research associate - CATI project
  • Hugo Dary - Former engineer - CATI project
  • Ludovic Fillon - Former engineer - CATI project
  • Mathieu Dubois - Former engineer - CATI project
  • Barbara Gris - Former PhD student
  • Géraldine Rousseau -Former PhD student
  • Marika Rudler -Former PhD student
  • Jean-Baptiste Schiratti - Former PhD student
  • Pietro Gori -Former PhD student - webpage
  • Ana Fouquier - -Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Formet engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain -Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia -Former clinical research associate - - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP
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  • Jeremy Guillon - Former PhD student - webpage
  • Michael Bacci - Former engineer
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow
  • Romain Valabrègue - Former Inserm Research Engineer
  • Eric Bardinet - Former CNRS Research Engineer - webpage
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage
  • Vera Dinkelacker - Former associated clinician
  • Thomas Estienne -  Former administrative assistant - CATI project
  • Chabha Azouani - Former clinical research associate - CATI project
  • Kelly Martineau - Former clinical research associate -CATI project
  • Sonia Djobeir - Former clinical research associate - CATI project
  • Hugo Dary - Former engineer - CATI project
  • Ludovic Fillon - Former engineer - CATI project
  • Mathieu Dubois - Former engineer - CATI project
  • Barbara Gris - Former PhD student
  • Géraldine Rousseau -Former PhD student
  • Marika Rudler - Former PhD student
  • Jean-Baptiste Schiratti - Former PhD student
  • Pietro Gori - Former PhD student - webpage
  • Ana Fouquier - Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Formet engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain -Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia -Former clinical research associate - - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP
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The Aramis Lab brings methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numerical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University and belongs to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
We are financially supported by these institutions.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical learning, Deep learning, Generative models, Bayesian models [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Magnetic resonance Imaging (MRI), Positron Emission Tomography (PET)[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Riemannian Geometry, diffeomorphisms[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Graph analysis, Multilayer networks, Network dynamics[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Non-linear mixed effects models, Riemannian geometry, Longitudinal shape models[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Clinical decision support systems, Disease progression models, Network alterations[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Modeling the presymptomatic phase in genetic forms of the disease[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Using multimodal neuroimaging to track disease progression[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Modeling disease progression, Computer-aided adjustment of treatment[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Using BCI for rehabilitation, BCI systems based on network analysis[/su_spoiler] [/su_accordion]
 
Team retreat at the villa Finaly, Florence, Italy
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    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
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Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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External collaborations

Methodological collaborations

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Local collaborations

Methodological collaborations

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Funding / main grants

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Faculty

[tmm name="faculty"]

Post-docs

[tmm name="post-docs"]

Engineers

[tmm name="engineers"]

PhD Students

[tmm name="phd-students"]

Associate fellows

[tmm name="collaborators"]

Former Members

  • Jeremy Guillon - Former PhD student - webpage
  • Michael Bacci - Former engineer, now at EURONEXT
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer, now at SED INRIA
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow, now researcher at MyBrainTechnologies
  • Romain Valabrègue - Former Inserm Research Engineer
  • Eric Bardinet - Former CNRS Research Engineer - webpage
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage
  • Vera Dinkelacker - Former associated clinician
  • Thomas Estienne -  Former administrative assistant
  • Chabha Azouani - Former clinical research associate
  • Kelly Martineau - Former clinical research associate
  • Sonia Djobeir - Former clinical research associate
  • Hugo Dary - Former engineer
  • Ludovic Fillon - Former engineer
  • Mathieu Dubois - Former engineer
  • Barbara Gris - Former PhD student
  • Géraldine Rousseau -Former PhD student
  • Marika Rudler - Former PhD student
  • Jean-Baptiste Schiratti - Former PhD student
  • Pietro Gori - Former PhD student - Now Assistant Professor, Telecom ParisTech - webpage
  • Ana Fouquier - Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Former engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Former clinical research associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2018-12-05 16:31:30', '2018-12-05 15:31:30', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '', (1565, 1, '2018-12-05 16:35:28', '2018-12-05 15:35:28', '

Clinica

Clinica
References
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
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Leasp

To complete References
  • To complete
[su_button url="https://gitlab.icm-institute.org/aramislab/leasp" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(at)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-12-05 16:35:28', '2018-12-05 15:35:28', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1566, 1, '2018-12-05 16:36:20', '2018-12-05 15:36:20', '

Clinica

Clinica
References  
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
 
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
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Leasp

To complete References
  • To complete
[su_button url="https://gitlab.icm-institute.org/aramislab/leasp" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(at)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-12-05 16:36:20', '2018-12-05 15:36:20', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1567, 1, '2018-12-05 16:36:49', '2018-12-05 15:36:49', '

Clinica

Clinica
References  
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
 
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Leasp

To complete References
  • To complete
[su_button url="https://gitlab.icm-institute.org/aramislab/leasp" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="stanley.durrleman(at)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-12-05 16:36:49', '2018-12-05 15:36:49', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1568, 7, '2018-12-05 17:10:21', '2018-12-05 16:10:21', '

Most representative publications

Networks

  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017.PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM 2017. PDF Paper in PDF
  • Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper Gonzalez. In Neuroimage ..., 2018. PDF Paper in PDF
  • Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 357-364. 2017. PDF Paper in PDF
  • Wei et al. . In MICCAI. 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinical studies

  • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
  • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
  • Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. PDF Paper in PDF
  • Dubois et al. . In Lancet in Neurology. ..., 2018. PDF Paper in PDF
  • Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-12-05 17:10:21', '2018-12-05 16:10:21', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1569, 7, '2018-12-05 17:13:24', '2018-12-05 16:13:24', '

Most representative publications

Networks

  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017.PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM 2017. PDF Paper in PDF
  • Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; ADNI; AIBL. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. In Neuroimage 2018. PDF Paper in PDF
  • Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 357-364. 2017. PDF Paper in PDF
  • Wei et al. . In MICCAI. 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinical studies

  • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
  • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
  • Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. PDF Paper in PDF
  • Dubois et al. . In Lancet in Neurology. ..., 2018. PDF Paper in PDF
  • Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-12-05 17:13:24', '2018-12-05 16:13:24', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1570, 7, '2018-12-05 17:14:39', '2018-12-05 16:14:39', '

Most representative publications

Networks

  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017.PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM 2017. PDF Paper in PDF
  • Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; ADNI; AIBL. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. In Neuroimage 2018. PDF Paper in PDF
  • Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 357-364. 2017. PDF Paper in PDF
  • Wei et al. . In MICCAI. 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinical studies

  • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
  • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
  • Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. PDF Paper in PDF
  • Dubois B, Epelbaum S, Nyasse F, Bakardjian H, Gagliardi G, Uspenskaya O, Houot M, Lista S, Cacciamani F, Potier MC, Bertrand A, Lamari F, Benali H, Mangin JF, Colliot O, Genthon R, Habert MO, Hampel H; INSIGHT-preAD study group. Cognitive and neuroimaging features and brain β-amyloidosis in individuals at risk of Alzheimer\'s disease (INSIGHT-preAD): a longitudinal observational study.. In Lancet in Neurology. 2018, Apr;17(4):335-346. PDF Paper in PDF
  • Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-12-05 17:14:39', '2018-12-05 16:14:39', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1572, 7, '2018-12-05 17:18:41', '2018-12-05 16:18:41', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-12-05 17:18:41', '2018-12-05 16:18:41', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1573, 7, '2018-12-05 17:19:03', '2018-12-05 16:19:03', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-12-05 17:19:03', '2018-12-05 16:19:03', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1574, 7, '2018-12-05 17:20:35', '2018-12-05 16:20:35', '', 'img_topics', '', 'inherit', 'closed', 'closed', '', 'img_topics', '', '', '2018-12-05 17:20:35', '2018-12-05 16:20:35', '', 22, 'https://www.aramislab.fr/wp-content/uploads/2018/12/img_topics.png', 0, 'attachment', 'image/png', (1575, 7, '2018-12-05 17:22:57', '2018-12-05 16:22:57', '

Clinica

Clinica
References  
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
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Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
 
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
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[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com,mario.chavez(at)upmc(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
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Leasp

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 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-12-05 17:22:57', '2018-12-05 16:22:57', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1576, 7, '2018-12-05 17:23:50', '2018-12-05 16:23:50', '

Clinica

Clinica
References  
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
 
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
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Leasp

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 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-12-05 17:23:50', '2018-12-05 16:23:50', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1577, 7, '2018-12-05 17:24:24', '2018-12-05 16:24:24', '

Clinica

Clinica
References  
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
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[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
 
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
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 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-12-05 17:24:24', '2018-12-05 16:24:24', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1578, 7, '2018-12-05 17:25:48', '2018-12-05 16:25:48', '

Clinica

Clinica
References  
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
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[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
 
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
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 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-12-05 17:25:48', '2018-12-05 16:25:48', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1579, 7, '2018-12-05 17:27:23', '2018-12-05 16:27:23', '

Clinica

Clinica
References  
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
 
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
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[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
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Leasp

To come soon References
  • To come soon
[su_button url="https://gitlab.icm-institute.org/aramislab/leasp" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
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 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-12-05 17:27:23', '2018-12-05 16:27:23', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1580, 7, '2018-12-05 17:28:16', '2018-12-05 16:28:16', '

Clinica

Clinica
References  
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
 
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
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Leasp

To come soon References
  • To come soon
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 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-12-05 17:28:16', '2018-12-05 16:28:16', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1581, 7, '2018-12-05 17:29:35', '2018-12-05 16:29:35', '

Clinica

Clinica
References  
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
 
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.  
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
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Leasp

To come soon References
  • To come soon
[su_button url="https://gitlab.icm-institute.org/aramislab/leasp" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
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 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-12-05 17:29:35', '2018-12-05 16:29:35', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1582, 7, '2018-12-05 17:29:53', '2018-12-05 16:29:53', '

Clinica

Clinica
References  
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage. 101(1): 35-49, 2014Link to paper
 
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.  
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Leasp

To come soon References
  • To come soon
 
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 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-12-05 17:29:53', '2018-12-05 16:29:53', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1583, 7, '2018-12-05 17:32:59', '2018-12-05 16:32:59', '', 'im_topics_2', '', 'inherit', 'closed', 'closed', '', 'im_topics_2', '', '', '2018-12-05 17:32:59', '2018-12-05 16:32:59', '', 0, 'https://www.aramislab.fr/wp-content/uploads/2018/12/im_topics_2.png', 0, 'attachment', 'image/png', (1584, 7, '2018-12-05 17:33:17', '2018-12-05 16:33:17', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-12-05 17:33:17', '2018-12-05 16:33:17', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1585, 7, '2018-12-05 17:35:12', '2018-12-05 16:35:12', '', 'img_topics_3', '', 'inherit', 'closed', 'closed', '', 'img_topics_3', '', '', '2018-12-05 17:35:12', '2018-12-05 16:35:12', '', 0, 'https://www.aramislab.fr/wp-content/uploads/2018/12/img_topics_3.png', 0, 'attachment', 'image/png', (1586, 7, '2018-12-05 17:35:27', '2018-12-05 16:35:27', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-12-05 17:35:27', '2018-12-05 16:35:27', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1587, 7, '2018-12-05 17:38:20', '2018-12-05 16:38:20', '

Most representative publications

Networks

  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017.PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM 2017. PDF Paper in PDF
  • Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; ADNI; AIBL. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. In Neuroimage 2018. PDF Paper in PDF
  • Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 357-364. 2017. PDF Paper in PDF
  • Wei, W., Poirion, E., Bodini, B., Durrleman, S., Ayache, N., Stankoff, B., Colliot, O. Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training. In MICCAI. 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinical studies

  • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
  • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
  • Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. PDF Paper in PDF
  • Dubois B, Epelbaum S, Nyasse F, Bakardjian H, Gagliardi G, Uspenskaya O, Houot M, Lista S, Cacciamani F, Potier MC, Bertrand A, Lamari F, Benali H, Mangin JF, Colliot O, Genthon R, Habert MO, Hampel H; INSIGHT-preAD study group. Cognitive and neuroimaging features and brain β-amyloidosis in individuals at risk of Alzheimer\'s disease (INSIGHT-preAD): a longitudinal observational study.. In Lancet in Neurology. 2018, Apr;17(4):335-346. PDF Paper in PDF
  • Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-12-05 17:38:20', '2018-12-05 16:38:20', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1588, 7, '2018-12-05 17:43:23', '2018-12-05 16:43:23', '

Most representative publications

Networks

  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017.PDF Paper in PDF
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Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM journal on Numerical Analysis 2017. 56(4), 256-2584PDF Paper in PDF
  • Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. PDF Paper in PDF
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Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3):682-696, 2013. PDF Paper in PDF
  • Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; ADNI; AIBL. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. In Neuroimage 2018. PDF Paper in PDF
  • Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 357-364. 2017. PDF Paper in PDF
  • Wei, W., Poirion, E., Bodini, B., Durrleman, S., Ayache, N., Stankoff, B., Colliot, O. Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training. In MICCAI. 2018. PDF Paper in PDF
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Clinical studies

  • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
  • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
  • Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. PDF Paper in PDF
  • Dubois B, Epelbaum S, Nyasse F, Bakardjian H, Gagliardi G, Uspenskaya O, Houot M, Lista S, Cacciamani F, Potier MC, Bertrand A, Lamari F, Benali H, Mangin JF, Colliot O, Genthon R, Habert MO, Hampel H; INSIGHT-preAD study group. Cognitive and neuroimaging features and brain β-amyloidosis in individuals at risk of Alzheimer\'s disease (INSIGHT-preAD): a longitudinal observational study.. In Lancet Neurol.. 2018, Apr;17(4):335-346. PDF Paper in PDF
  • Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2018-12-05 17:43:23', '2018-12-05 16:43:23', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1589, 7, '2018-12-05 17:43:26', '2018-12-05 16:43:26', '

Clinica

Clinica
References
  • Routier A, et al, In OHBM 2018. PDF Paper in PDF
  • Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; ADNI; AIBL. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. In Neuroimage 2018. PDF Paper in PDF
 
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
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Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman et al.Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 PDF Paper in PDF
 
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
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Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.  
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
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Leasp

To come soon References
  • To come soon
 
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 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-12-05 17:43:26', '2018-12-05 16:43:26', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1590, 7, '2018-12-05 17:43:56', '2018-12-05 16:43:56', '

Clinica

Clinica
References
  • Routier A, et al, In OHBM 2018. PDF Paper in PDF
  • Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; ADNI; AIBL. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. In Neuroimage 2018. PDF Paper in PDF
 
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman et al.Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 PDF Paper in PDF
 
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.  
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
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Leasp

To come soon References
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
 
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 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-12-05 17:43:56', '2018-12-05 16:43:56', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1591, 7, '2018-12-05 17:44:11', '2018-12-05 16:44:11', '

Clinica

Clinica
References
  • Routier A, et al, In OHBM 2018. PDF Paper in PDF
  • Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; ADNI; AIBL. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. In Neuroimage 2018. PDF Paper in PDF
 
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 PDF Paper in PDF
 
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.  
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
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Leasp

To come soon References
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
 
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 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-12-05 17:44:11', '2018-12-05 16:44:11', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1592, 7, '2018-12-05 17:46:22', '2018-12-05 16:46:22', '

Clinica

Clinica
References
  • Routier A, et al, In OHBM 2018. PDF Paper in PDF
  • Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O; ADNI; AIBL. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. In Neuroimage 2018. PDF Paper in PDF
 
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[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 PDF Paper in PDF
  • Bône, A., Louis, M., Martin, B., & Durrleman, S. Deformetrica 4: an open-source software for statistical shape analysis. In nternational Workshop on Shape in Medical Imaging Springer, Cham, 2018. p. 3-13. PDF Paper in PDF
 
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[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.  
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
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Leasp

To come soon References
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
 
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    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
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New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
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Network theoretic approaches to integrate heterogeneous brain networks

T
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Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-12-05 17:49:54', '2018-12-05 16:49:54', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1595, 7, '2018-12-05 17:54:30', '2018-12-05 16:54:30', '

Clinica

Clinica
References
  • Routier, A., Guillon, J., Burgos, N., Samper-Gonzalez, J., Wen, J., Fontanella, S., ... & Lu, P. In Annual meeting of the Organization for Human Brain Mapping-OHBM 2018. PDF Paper in PDF
 
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 PDF Paper in PDF
  • Bône, A., Louis, M., Martin, B., & Durrleman, S. Deformetrica 4: an open-source software for statistical shape analysis. In nternational Workshop on Shape in Medical Imaging Springer, Cham, 2018. p. 3-13. PDF Paper in PDF
 
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.  
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Leasp

To come soon References
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
 
[su_button url="https://gitlab.icm-institute.org/aramislab/leasp" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:stanley.durrleman(at)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-12-05 17:54:30', '2018-12-05 16:54:30', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1596, 7, '2018-12-06 10:23:51', '2018-12-06 09:23:51', '

Clinica

Clinica
References
  • Routier, A., Guillon, J., Burgos, N., Samper-Gonzalez, J., Wen, J., Fontanella, S., Bottani, S., Jacquemont, T., Marcoux, A., Gori, P., Lu, P., Moreau, T., Bacci, M., Durrleman, S., Colliot, O. Clinica: an open source software platform for reproducible clinical neuroscience studies. In Annual meeting of the Organization for Human Brain Mapping-OHBM 2018. PDF Paper in PDF
 
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 PDF Paper in PDF
  • Bône, A., Louis, M., Martin, B., & Durrleman, S. Deformetrica 4: an open-source software for statistical shape analysis. In nternational Workshop on Shape in Medical Imaging Springer, Cham, 2018. p. 3-13. PDF Paper in PDF
 
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.  
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Leasp

To come soon References
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
 
[su_button url="https://gitlab.icm-institute.org/aramislab/leasp" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:stanley.durrleman(at)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2018-12-06 10:23:51', '2018-12-06 09:23:51', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1597, 7, '2018-12-06 10:25:32', '2018-12-06 09:25:32', '

Clinica

Clinica
References
  • Routier, A., Guillon, J., Burgos, N., Samper-Gonzalez, J., Wen, J., Fontanella, S., Bottani, S., Jacquemont, T., Marcoux, A., Gori, P., Lu, P., Moreau, T., Bacci, M., Durrleman, S., Colliot, O. Clinica: an open source software platform for reproducible clinical neuroscience studies. In Annual meeting of the Organization for Human Brain Mapping-OHBM 2018. PDF Paper in PDF
 
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman, S., Prastawa, M., Charon, N., Korenberg, J.R., Joshi, S., Gerig, G., Trouvé, A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 PDF Paper in PDF
  • Bône, A., Louis, M., Martin, B., & Durrleman, S. Deformetrica 4: an open-source software for statistical shape analysis. In nternational Workshop on Shape in Medical Imaging Springer, Cham, 2018. p. 3-13. PDF Paper in PDF
 
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.  
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Leasp

To come soon References
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
 
[su_button url="https://gitlab.icm-institute.org/aramislab/leasp" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:stanley.durrleman(at)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
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    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-revision-v1', '', '', '2018-12-07 10:53:34', '2018-12-07 09:53:34', '', 22, 'https://www.aramislab.fr/22-revision-v1/', 0, 'revision', '', (1601, 7, '2018-12-07 10:54:38', '2018-12-07 09:54:38', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed to capture various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, Multiple Sclerosis, Parkinson\'s disease...). They shall allow to deepen our understanding of neurological diseases and to develop new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

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Clinica

Clinica
References
  • Routier, A., Guillon, J., Burgos, N., Samper-Gonzalez, J., Wen, J., Fontanella, S., Bottani, S., Jacquemont, T., Marcoux, A., Gori, P., Lu, P., Moreau, T., Bacci, M., Durrleman, S., Colliot, O. Clinica: an open source software platform for reproducible clinical neuroscience studies. In Annual meeting of the Organization for Human Brain Mapping-OHBM 2018. PDF Paper in PDF
 
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman, S., Prastawa, M., Charon, N., Korenberg, J.R., Joshi, S., Gerig, G., Trouvé, A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 PDF Paper in PDF
  • Bône, A., Louis, M., Martin, B., & Durrleman, S. Deformetrica 4: an open-source software for statistical shape analysis. In nternational Workshop on Shape in Medical Imaging Springer, Cham, 2018. p. 3-13. PDF Paper in PDF
 
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory.  
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Leasp

To come soon References
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
 
[su_button url="https://gitlab.icm-institute.org/aramislab/leasp" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:stanley.durrleman(at)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
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Faculty

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Post-docs

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PhD Students

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Engineers

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Associate fellows

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Former Members

  • Jeremy Guillon - Former PhD student - webpage
  • Michael Bacci - Former engineer, now at EURONEXT
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer, now at SED INRIA
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow, now researcher at MyBrainTechnologies
  • Romain Valabrègue - Former Inserm Research Engineer
  • Eric Bardinet - Former CNRS Research Engineer - webpage
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage
  • Vera Dinkelacker - Former associated clinician
  • Thomas Estienne -  Former administrative assistant
  • Chabha Azouani - Former clinical research associate
  • Kelly Martineau - Former clinical research associate
  • Sonia Djobeir - Former clinical research associate
  • Hugo Dary - Former engineer
  • Ludovic Fillon - Former engineer
  • Mathieu Dubois - Former engineer
  • Barbara Gris - Former PhD student
  • Géraldine Rousseau -Former PhD student
  • Marika Rudler - Former PhD student
  • Jean-Baptiste Schiratti - Former PhD student
  • Pietro Gori - Former PhD student - Now Assistant Professor, Telecom ParisTech - webpage
  • Ana Fouquier - Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Former engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Former clinical research associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP
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Faculty

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Post-docs

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PhD Students

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Engineers

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Associate fellows

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Administrative staff

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Former Members

  • Jeremy Guillon - Former PhD student - webpage
  • Michael Bacci - Former engineer, now at EURONEXT
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer, now at SED INRIA
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow, now researcher at MyBrainTechnologies
  • Romain Valabrègue - Former Inserm Research Engineer
  • Eric Bardinet - Former CNRS Research Engineer - webpage
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage
  • Vera Dinkelacker - Former associated clinician
  • Thomas Estienne -  Former administrative assistant
  • Chabha Azouani - Former clinical research associate
  • Kelly Martineau - Former clinical research associate
  • Sonia Djobeir - Former clinical research associate
  • Hugo Dary - Former engineer
  • Ludovic Fillon - Former engineer
  • Mathieu Dubois - Former engineer
  • Barbara Gris - Former PhD student
  • Géraldine Rousseau -Former PhD student
  • Marika Rudler - Former PhD student
  • Jean-Baptiste Schiratti - Former PhD student
  • Pietro Gori - Former PhD student - Now Assistant Professor, Telecom ParisTech - webpage
  • Ana Fouquier - Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Former engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Former clinical research associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP
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Faculty

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Post-docs

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PhD Students

[tmm name="phd-students"]

Engineers

[tmm name="engineers"]

Associate fellows

[tmm name="collaborators"]

Administrative staff

[tmm name="assistant"]

Former Members

  • Catalina Obando - Former PhD student
  • Jeremy Guillon - Former PhD student - webpage
  • Michael Bacci - Former engineer, now at EURONEXT
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer, now at SED INRIA
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow, now researcher at MyBrainTechnologies
  • Romain Valabrègue - Former Inserm Research Engineer
  • Eric Bardinet - Former CNRS Research Engineer - webpage
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage
  • Vera Dinkelacker - Former associated clinician
  • Thomas Estienne -  Former administrative assistant
  • Chabha Azouani - Former clinical research associate
  • Kelly Martineau - Former clinical research associate
  • Sonia Djobeir - Former clinical research associate
  • Hugo Dary - Former engineer
  • Ludovic Fillon - Former engineer
  • Mathieu Dubois - Former engineer
  • Barbara Gris - Former PhD student
  • Géraldine Rousseau -Former PhD student
  • Marika Rudler - Former PhD student
  • Jean-Baptiste Schiratti - Former PhD student
  • Pietro Gori - Former PhD student - Now Assistant Professor, Telecom ParisTech - webpage
  • Ana Fouquier - Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Former engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Former clinical research associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP
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If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).

Postdocs / Scientists

PhD thesis

Engineers / Software developers

', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2019-07-22 14:52:18', '2019-07-22 13:52:18', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '', (1626, 7, '2019-07-22 14:53:00', '2019-07-22 13:53:00', '
If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).

Postdocs / Scientists

PhD thesis

Engineers / Software developers

', 'Job offers', '', 'inherit', 'closed', 'closed', '', '30-revision-v1', '', '', '2019-07-22 14:53:00', '2019-07-22 13:53:00', '', 30, 'https://www.aramislab.fr/30-revision-v1/', 0, 'revision', '', (1627, 7, '2019-07-26 14:38:37', '2019-07-26 13:38:37', '', 'dariosaracino', '', 'inherit', 'closed', 'closed', '', 'dariosaracino', '', '', '2019-07-26 14:38:37', '2019-07-26 13:38:37', '', 0, 'https://www.aramislab.fr/wp-content/uploads/2019/07/dariosaracino.png', 0, 'attachment', 'image/png', (1629, 7, '2019-08-05 09:14:45', '2019-08-05 08:14:45', '[su_slider source="media: 968,966,1015,958,971" width="1600" height="600" title="no" mousewheel="no"]
The Aramis Lab brings together methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numerical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University and belongs to the Brain and Spinal cord Institute (ICM) which is a recently created neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
We are financially supported by these institutions.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical learning, Deep learning, Generative models, Bayesian models [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Magnetic resonance Imaging (MRI), Positron Emission Tomography (PET)[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Riemannian Geometry, diffeomorphisms[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Graph analysis, Multilayer networks, Network dynamics[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Non-linear mixed effects models, Riemannian geometry, Longitudinal shape models[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Clinical decision support systems, Disease progression models, Network alterations[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Modeling the presymptomatic phase in genetic forms of the disease[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Using multimodal neuroimaging to track disease progression[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Modeling disease progression, Computer-aided adjustment of treatment[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Using BCI for rehabilitation, BCI systems based on network analysis[/su_spoiler] [/su_accordion]
 
Team retreat at the villa Finaly, Florence, Italy
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7T MRI of the hippocampus

This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus. It will be continuously updated with new code, stay tuned!

Slab registration

Code to register multiple slabs in order to create a single-slab high resolution volume. Code is available here.

Manual segmentation of hippocampal subregions

Coming soon!

Hippocampal thickness measurement

Coming soon!

About

These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Université Pierre et Marie Curie). They are distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses. If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'publish', 'closed', 'closed', '', 'seven-hipp', '', '', '2019-10-25 16:35:38', '2019-10-25 15:35:38', '', 0, 'https://www.aramislab.fr/?page_id=1635', 0, 'page', '', (1636, 7, '2019-10-25 15:58:28', '2019-10-25 14:58:28', '## 7T MRI of the hippocampus This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus. It will be continuously updated with new code, stay tuned! Slab registration Code to register multiple slabs in order to create a single-slab high resolution volume. Code is available here. Manual segmentation of hippocampal subregions Coming soon! Hippocampal thickness measurement Coming soon! About These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Universit� Pierre et Marie Curie). They distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses. If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'inherit', 'closed', 'closed', '', '1635-revision-v1', '', '', '2019-10-25 15:58:28', '2019-10-25 14:58:28', '', 1635, 'https://www.aramislab.fr/1635-revision-v1/', 0, 'revision', '', (1637, 7, '2019-10-25 16:02:35', '2019-10-25 15:02:35', '

7T MRI of the hippocampus

This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus. It will be continuously updated with new code, stay tuned!

Slab registration

Code to register multiple slabs in order to create a single-slab high resolution volume. Code is available here.

Manual segmentation of hippocampal subregions

Coming soon!

Hippocampal thickness measurement

Coming soon!

About

These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Universit� Pierre et Marie Curie). They distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses. If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'inherit', 'closed', 'closed', '', '1635-autosave-v1', '', '', '2019-10-25 16:02:35', '2019-10-25 15:02:35', '', 1635, 'https://www.aramislab.fr/1635-autosave-v1/', 0, 'revision', '', (1638, 7, '2019-10-25 16:01:46', '2019-10-25 15:01:46', '

7T MRI of the hippocampus

This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus. It will be continuously updated with new code, stay tuned! Slab registration Code to register multiple slabs in order to create a single-slab high resolution volume. Code is available here. Manual segmentation of hippocampal subregions Coming soon! Hippocampal thickness measurement Coming soon! About These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Universit� Pierre et Marie Curie). They distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses. If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'inherit', 'closed', 'closed', '', '1635-revision-v1', '', '', '2019-10-25 16:01:46', '2019-10-25 15:01:46', '', 1635, 'https://www.aramislab.fr/1635-revision-v1/', 0, 'revision', '', (1639, 7, '2019-10-25 16:02:36', '2019-10-25 15:02:36', '

7T MRI of the hippocampus

This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus. It will be continuously updated with new code, stay tuned!

Slab registration

Code to register multiple slabs in order to create a single-slab high resolution volume. Code is available here.

Manual segmentation of hippocampal subregions

Coming soon!

Hippocampal thickness measurement

Coming soon!

About

These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Universit� Pierre et Marie Curie). They distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses. If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'inherit', 'closed', 'closed', '', '1635-revision-v1', '', '', '2019-10-25 16:02:36', '2019-10-25 15:02:36', '', 1635, 'https://www.aramislab.fr/1635-revision-v1/', 0, 'revision', '', (1640, 7, '2019-10-25 16:02:47', '2019-10-25 15:02:47', '

7T MRI of the hippocampus

This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus. It will be continuously updated with new code, stay tuned!

Slab registration

Code to register multiple slabs in order to create a single-slab high resolution volume. Code is available here.

Manual segmentation of hippocampal subregions

Coming soon!

Hippocampal thickness measurement

Coming soon!

About

These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Universit� Pierre et Marie Curie). They distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses. If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'inherit', 'closed', 'closed', '', '1635-revision-v1', '', '', '2019-10-25 16:02:47', '2019-10-25 15:02:47', '', 1635, 'https://www.aramislab.fr/1635-revision-v1/', 0, 'revision', '', (1641, 7, '2019-10-25 16:03:20', '2019-10-25 15:03:20', '', 'sevenhipp', '', 'inherit', 'closed', 'closed', '', 'sevenhipp', '', '', '2019-10-25 16:03:20', '2019-10-25 15:03:20', '', 0, 'https://www.aramislab.fr/wp-content/uploads/2019/10/sevenhipp.png', 0, 'attachment', 'image/png', (1642, 7, '2019-10-25 16:03:49', '2019-10-25 15:03:49', '

7T MRI of the hippocampus

This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus. It will be continuously updated with new code, stay tuned!

Slab registration

Code to register multiple slabs in order to create a single-slab high resolution volume. Code is available here.

Manual segmentation of hippocampal subregions

Coming soon!

Hippocampal thickness measurement

Coming soon!

About

These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Universit� Pierre et Marie Curie). They distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses. If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'inherit', 'closed', 'closed', '', '1635-revision-v1', '', '', '2019-10-25 16:03:49', '2019-10-25 15:03:49', '', 1635, 'https://www.aramislab.fr/1635-revision-v1/', 0, 'revision', '', (1643, 7, '2019-10-25 16:04:14', '2019-10-25 15:04:14', '

7T MRI of the hippocampus

This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus. It will be continuously updated with new code, stay tuned!

Slab registration

Code to register multiple slabs in order to create a single-slab high resolution volume. Code is available here.

Manual segmentation of hippocampal subregions

Coming soon!

Hippocampal thickness measurement

Coming soon!

About

These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Université Pierre et Marie Curie). They distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses. If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'inherit', 'closed', 'closed', '', '1635-revision-v1', '', '', '2019-10-25 16:04:14', '2019-10-25 15:04:14', '', 1635, 'https://www.aramislab.fr/1635-revision-v1/', 0, 'revision', '', (1644, 7, '2019-10-25 16:04:45', '2019-10-25 15:04:45', '

7T MRI of the hippocampus

This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus. It will be continuously updated with new code, stay tuned!

Slab registration

Code to register multiple slabs in order to create a single-slab high resolution volume. Code is available here.

Manual segmentation of hippocampal subregions

Coming soon!

Hippocampal thickness measurement

Coming soon!

About

These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Université Pierre et Marie Curie). They are distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses. If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'inherit', 'closed', 'closed', '', '1635-revision-v1', '', '', '2019-10-25 16:04:45', '2019-10-25 15:04:45', '', 1635, 'https://www.aramislab.fr/1635-revision-v1/', 0, 'revision', '', (1645, 7, '2019-10-25 16:16:16', '2019-10-25 15:16:16', '

7T MRI of the hippocampus

This webpage contains ressources (code, atlases...) for high resolution imaging of the internal structure of the hippocampus. It will be continuously updated with new code, stay tuned!

Slab registration

Code to register multiple slabs in order to create a single-slab high resolution volume. Code is available here.

Manual segmentation of hippocampal subregions

Coming soon!

Hippocampal thickness measurement

Coming soon!

About

These ressources have been created at the ARAMIS Lab (ICM, Brain and Spinal Cord Institute, CNRS, Inria, Inserm, Université Pierre et Marie Curie). They are distributed under the terms of the INRIA Non-Commercial License Agreement given here. This is a non-free license that grants you the right to use these ressources for educational, research or evaluation purposes only, but prohibits commercial uses. If you use these ressources, we kindly ask you to cite the corresponding papers (references to cite are indicated in each section).', 'Seven Hipp', '', 'inherit', 'closed', 'closed', '', '1635-revision-v1', '', '', '2019-10-25 16:16:16', '2019-10-25 15:16:16', '', 1635, 'https://www.aramislab.fr/1635-revision-v1/', 0, 'revision', '', (1649, 7, '2019-11-05 09:44:48', '2019-11-05 08:44:48', '', 'etiennemaheux', '', 'inherit', 'closed', 'closed', '', 'etiennemaheux', '', '', '2019-11-05 09:44:48', '2019-11-05 08:44:48', '', 1179, 'https://www.aramislab.fr/wp-content/uploads/2018/11/etiennemaheux.png', 0, 'attachment', 'image/png', (1650, 7, '2019-11-05 11:12:31', '2019-11-05 10:12:31', '', 'thomas_nedelec', '', 'inherit', 'closed', 'closed', '', 'thomas_nedelec', '', '', '2019-11-05 11:13:32', '2019-11-05 10:13:32', '', 1179, 'https://www.aramislab.fr/wp-content/uploads/2018/11/thomas_nedelec.jpg', 0, 'attachment', 'image/jpeg', (1657, 8, '2020-04-07 17:30:57', '2020-04-07 16:30:57', '

Faculty

[tmm name="faculty"]

Post-docs

[tmm name="post-docs"]

PhD Students

[tmm name="phd-students"]

Engineers

[tmm name="engineers"]

Associate fellows

[tmm name="collaborators"]

Administrative staff

[tmm name="assistant"]

Former Members

  • Catalina Obando - Former PhoD student
  • Jeremy Guillon - Former PhD student - webpage
  • Michael Bacci - Former engineer, now at EURONEXT
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer, now at SED INRIA
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow, now researcher at MyBrainTechnologies
  • Romain Valabrègue - Former Inserm Research Engineer
  • Eric Bardinet - Former CNRS Research Engineer - webpage
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage
  • Vera Dinkelacker - Former associated clinician
  • Thomas Estienne -  Former administrative assistant
  • Chabha Azouani - Former clinical research associate
  • Kelly Martineau - Former clinical research associate
  • Sonia Djobeir - Former clinical research associate
  • Hugo Dary - Former engineer
  • Ludovic Fillon - Former engineer
  • Mathieu Dubois - Former engineer
  • Barbara Gris - Former PhD student
  • Géraldine Rousseau -Former PhD student
  • Marika Rudler - Former PhD student
  • Jean-Baptiste Schiratti - Former PhD student
  • Pietro Gori - Former PhD student - Now Assistant Professor, Telecom ParisTech - webpage
  • Ana Fouquier - Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Former engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Former clinical research associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2020-04-07 17:30:57', '2020-04-07 16:30:57', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '', (1658, 8, '2020-09-22 16:07:40', '2020-09-22 15:07:40', '

Faculty

[tmm name="faculty"]

Post-docs

[tmm name="post-docs"]

PhD Students

[tmm name="phd-students"]

Engineers

[tmm name="engineers"]

Associate fellows

[tmm name="collaborators"]

Administrative staff

[tmm name="assistant"]

Former Members

Left in 2020 :
  • Alexandre Bône - Former PhD student. Now Augmented Intelligence Specialist at Guerbet
  • Manon Ansart - Former PhD student. Now Postdoctoral fellow at the LIO laboratory, hosted at ETS, Montreal
  • Arnaud Marcoux - Former engineer
  • Adam Wild - Former Engineer
  • Vincent Henry - Former Postdoctoral fellow
  • Emmannuel Mauduit  - LinkedIn - Former coordinator the ICM center of  Neuroinformatics
  Left in 2019 :
  • Maxime Louis - Former PhD Student, now at Pixyl
  • Pascal Lu - Former PhD student
  • Benoit Martin - Former engineer. Now PhD student
  • Alexis Guyot - Former Postdoctoral fellow
  • Junhao Wen - Former PhD student
  • Fanny Grosselin - Former PhD student
  • Jorge Samper-González - Former PhD student & Postdoctoral fellow, now data scientist at Qynapse
  Left in 2018 and before :
  • Catalina Obando - Former PhD student
  • Jeremy Guillon - Former PhD student - webpage
  • Michael Bacci - Former engineer, now at EURONEXT
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer, now at SED INRIA
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow, now researcher at MyBrainTechnologies
  • Romain Valabrègue - Former Inserm Research Engineer
  • Eric Bardinet - Former CNRS Research Engineer - webpage
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage
  • Vera Dinkelacker - Former associated clinician
  • Thomas Estienne -  Former administrative assistant
  • Chabha Azouani - Former clinical research associate
  • Kelly Martineau - Former clinical research associate
  • Sonia Djobeir - Former clinical research associate
  • Hugo Dary - Former engineer
  • Ludovic Fillon - Former engineer
  • Mathieu Dubois - Former engineer
  • Barbara Gris - Former PhD student
  • Géraldine Rousseau -Former PhD student
  • Marika Rudler - Former PhD student
  • Jean-Baptiste Schiratti - Former PhD student
  • Pietro Gori - Former PhD student - Now Assistant Professor, Telecom ParisTech - webpage
  • Ana Fouquier - Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Former engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Former clinical research associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-autosave-v1', '', '', '2020-09-22 16:07:40', '2020-09-22 15:07:40', '', 4, 'https://www.aramislab.fr/4-autosave-v1/', 0, 'revision', '', (1659, 8, '2020-04-07 17:32:53', '2020-04-07 16:32:53', '

Faculty

[tmm name="faculty"]

Post-docs

[tmm name="post-docs"]

PhD Students

[tmm name="phd-students"]

Engineers

[tmm name="engineers"]

Associate fellows

[tmm name="collaborators"]

Administrative staff

[tmm name="assistant"]

Former Members

Left in 2020 :
  • Alexandre Bône - Former PhD student
  • Manon Ansart - Former PhD student. Now Postdoctoral fellow at the LIO laboratory, hosted at ETS, Montreal
  • Arnaud Marcoux - Former engineer
Left in 2019 :
  • Maxime Louis - Former PhD Student, now at Pixyl
  • Pascal Lu - Former PhD student
  • Benoit Martin - Former engineer. Now PhD student
  • Alexis Guyot - Former Postdoctoral fellow
  • Junhao Wen - Former PhD student
  • Fanny Grosselin - Former PhD student
  • Jorge Samper-González - Former PhD student & Postdoctoral fellow, now data scientist at Qynapse
Left in 2018 and before :
  • Catalina Obando - Former PhD student
  • Jeremy Guillon - Former PhD student - webpage
  • Michael Bacci - Former engineer, now at EURONEXT
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer, now at SED INRIA
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow, now researcher at MyBrainTechnologies
  • Romain Valabrègue - Former Inserm Research Engineer
  • Eric Bardinet - Former CNRS Research Engineer - webpage
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage
  • Vera Dinkelacker - Former associated clinician
  • Thomas Estienne -  Former administrative assistant
  • Chabha Azouani - Former clinical research associate
  • Kelly Martineau - Former clinical research associate
  • Sonia Djobeir - Former clinical research associate
  • Hugo Dary - Former engineer
  • Ludovic Fillon - Former engineer
  • Mathieu Dubois - Former engineer
  • Barbara Gris - Former PhD student
  • Géraldine Rousseau -Former PhD student
  • Marika Rudler - Former PhD student
  • Jean-Baptiste Schiratti - Former PhD student
  • Pietro Gori - Former PhD student - Now Assistant Professor, Telecom ParisTech - webpage
  • Ana Fouquier - Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Former engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Former clinical research associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2020-04-07 17:32:53', '2020-04-07 16:32:53', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '', (1660, 8, '2020-04-07 17:33:19', '2020-04-07 16:33:19', '

Faculty

[tmm name="faculty"]

Post-docs

[tmm name="post-docs"]

PhD Students

[tmm name="phd-students"]

Engineers

[tmm name="engineers"]

Associate fellows

[tmm name="collaborators"]

Administrative staff

[tmm name="assistant"]

Former Members

Left in 2020 :
  • Alexandre Bône - Former PhD student
  • Manon Ansart - Former PhD student. Now Postdoctoral fellow at the LIO laboratory, hosted at ETS, Montreal
  • Arnaud Marcoux - Former engineer
Left in 2019 :
  • Maxime Louis - Former PhD Student, now at Pixyl
  • Pascal Lu - Former PhD student
  • Benoit Martin - Former engineer. Now PhD student
  • Alexis Guyot - Former Postdoctoral fellow
  • Junhao Wen - Former PhD student
  • Fanny Grosselin - Former PhD student
  • Jorge Samper-González - Former PhD student & Postdoctoral fellow, now data scientist at Qynapse
Left in 2018 and before :
  • Catalina Obando - Former PhD student
  • Jeremy Guillon - Former PhD student - webpage
  • Michael Bacci - Former engineer, now at EURONEXT
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer, now at SED INRIA
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow, now researcher at MyBrainTechnologies
  • Romain Valabrègue - Former Inserm Research Engineer
  • Eric Bardinet - Former CNRS Research Engineer - webpage
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage
  • Vera Dinkelacker - Former associated clinician
  • Thomas Estienne -  Former administrative assistant
  • Chabha Azouani - Former clinical research associate
  • Kelly Martineau - Former clinical research associate
  • Sonia Djobeir - Former clinical research associate
  • Hugo Dary - Former engineer
  • Ludovic Fillon - Former engineer
  • Mathieu Dubois - Former engineer
  • Barbara Gris - Former PhD student
  • Géraldine Rousseau -Former PhD student
  • Marika Rudler - Former PhD student
  • Jean-Baptiste Schiratti - Former PhD student
  • Pietro Gori - Former PhD student - Now Assistant Professor, Telecom ParisTech - webpage
  • Ana Fouquier - Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Former engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Former clinical research associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP
', 'Team Members', '', 'inherit', 'closed', 'closed', '', '4-revision-v1', '', '', '2020-04-07 17:33:19', '2020-04-07 16:33:19', '', 4, 'https://www.aramislab.fr/4-revision-v1/', 0, 'revision', '', (1661, 8, '2020-04-07 17:35:03', '2020-04-07 16:35:03', '

Faculty

[tmm name="faculty"]

Post-docs

[tmm name="post-docs"]

PhD Students

[tmm name="phd-students"]

Engineers

[tmm name="engineers"]

Associate fellows

[tmm name="collaborators"]

Administrative staff

[tmm name="assistant"]

Former Members

Left in 2020 :
  • Alexandre Bône - Former PhD student
  • Manon Ansart - Former PhD student. Now Postdoctoral fellow at the LIO laboratory, hosted at ETS, Montreal
  • Arnaud Marcoux - Former engineer
Left in 2019 :
  • Maxime Louis - Former PhD Student, now at Pixyl
  • Pascal Lu - Former PhD student
  • Benoit Martin - Former engineer. Now PhD student
  • Alexis Guyot - Former Postdoctoral fellow
  • Junhao Wen - Former PhD student
  • Fanny Grosselin - Former PhD student
  • Jorge Samper-González - Former PhD student & Postdoctoral fellow, now data scientist at Qynapse
Left in 2018 and before :
  • Catalina Obando - Former PhD student
  • Jeremy Guillon - Former PhD student - webpage
  • Michael Bacci - Former engineer, now at EURONEXT
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer, now at SED INRIA
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow, now researcher at MyBrainTechnologies
  • Romain Valabrègue - Former Inserm Research Engineer
  • Eric Bardinet - Former CNRS Research Engineer - webpage
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage
  • Vera Dinkelacker - Former associated clinician
  • Thomas Estienne -  Former administrative assistant
  • Chabha Azouani - Former clinical research associate
  • Kelly Martineau - Former clinical research associate
  • Sonia Djobeir - Former clinical research associate
  • Hugo Dary - Former engineer
  • Ludovic Fillon - Former engineer
  • Mathieu Dubois - Former engineer
  • Barbara Gris - Former PhD student
  • Géraldine Rousseau -Former PhD student
  • Marika Rudler - Former PhD student
  • Jean-Baptiste Schiratti - Former PhD student
  • Pietro Gori - Former PhD student - Now Assistant Professor, Telecom ParisTech - webpage
  • Ana Fouquier - Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Former engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Former clinical research associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP
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Faculty

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Post-docs

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PhD Students

[tmm name="phd-students"]

Engineers

[tmm name="engineers"]

Associate fellows

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Administrative staff

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Former Members

Left in 2020 :
  • Alexandre Bône - Former PhD student
  • Manon Ansart - Former PhD student. Now Postdoctoral fellow at the LIO laboratory, hosted at ETS, Montreal
  • Arnaud Marcoux - Former engineer
  Left in 2019 :
  • Maxime Louis - Former PhD Student, now at Pixyl
  • Pascal Lu - Former PhD student
  • Benoit Martin - Former engineer. Now PhD student
  • Alexis Guyot - Former Postdoctoral fellow
  • Junhao Wen - Former PhD student
  • Fanny Grosselin - Former PhD student
  • Jorge Samper-González - Former PhD student & Postdoctoral fellow, now data scientist at Qynapse
  Left in 2018 and before :
  • Catalina Obando - Former PhD student
  • Jeremy Guillon - Former PhD student - webpage
  • Michael Bacci - Former engineer, now at EURONEXT
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer, now at SED INRIA
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow, now researcher at MyBrainTechnologies
  • Romain Valabrègue - Former Inserm Research Engineer
  • Eric Bardinet - Former CNRS Research Engineer - webpage
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage
  • Vera Dinkelacker - Former associated clinician
  • Thomas Estienne -  Former administrative assistant
  • Chabha Azouani - Former clinical research associate
  • Kelly Martineau - Former clinical research associate
  • Sonia Djobeir - Former clinical research associate
  • Hugo Dary - Former engineer
  • Ludovic Fillon - Former engineer
  • Mathieu Dubois - Former engineer
  • Barbara Gris - Former PhD student
  • Géraldine Rousseau -Former PhD student
  • Marika Rudler - Former PhD student
  • Jean-Baptiste Schiratti - Former PhD student
  • Pietro Gori - Former PhD student - Now Assistant Professor, Telecom ParisTech - webpage
  • Ana Fouquier - Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Former engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Former clinical research associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP
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Faculty

[tmm name="faculty"]

Post-docs

[tmm name="post-docs"]

PhD Students

[tmm name="phd-students"]

Engineers

[tmm name="engineers"]

Associate fellows

[tmm name="collaborators"]

Administrative staff

[tmm name="assistant"]

Former Members

Left in 2020 :
  • Alexandre Bône - Former PhD student. Now Augmented Intelligence Specialist at Guerbet
  • Manon Ansart - Former PhD student. Now Postdoctoral fellow at the LIO laboratory, hosted at ETS, Montreal
  • Arnaud Marcoux - Former engineer
  Left in 2019 :
  • Maxime Louis - Former PhD Student, now at Pixyl
  • Pascal Lu - Former PhD student
  • Benoit Martin - Former engineer. Now PhD student
  • Alexis Guyot - Former Postdoctoral fellow
  • Junhao Wen - Former PhD student
  • Fanny Grosselin - Former PhD student
  • Jorge Samper-González - Former PhD student & Postdoctoral fellow, now data scientist at Qynapse
  Left in 2018 and before :
  • Catalina Obando - Former PhD student
  • Jeremy Guillon - Former PhD student - webpage
  • Michael Bacci - Former engineer, now at EURONEXT
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer, now at SED INRIA
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow, now researcher at MyBrainTechnologies
  • Romain Valabrègue - Former Inserm Research Engineer
  • Eric Bardinet - Former CNRS Research Engineer - webpage
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage
  • Vera Dinkelacker - Former associated clinician
  • Thomas Estienne -  Former administrative assistant
  • Chabha Azouani - Former clinical research associate
  • Kelly Martineau - Former clinical research associate
  • Sonia Djobeir - Former clinical research associate
  • Hugo Dary - Former engineer
  • Ludovic Fillon - Former engineer
  • Mathieu Dubois - Former engineer
  • Barbara Gris - Former PhD student
  • Géraldine Rousseau -Former PhD student
  • Marika Rudler - Former PhD student
  • Jean-Baptiste Schiratti - Former PhD student
  • Pietro Gori - Former PhD student - Now Assistant Professor, Telecom ParisTech - webpage
  • Ana Fouquier - Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Former engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Former clinical research associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP
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If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).

Postdocs / Scientists

PhD thesis

Engineers / Software developers

 Master Internships / Stages de Master

  • See PhD thesis above
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If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).

Postdocs / Scientists

PhD thesis

Engineers / Software developers

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If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).

Postdocs / Scientists

PhD thesis

Engineers / Software developers

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If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and varied expertise (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).

Postdocs / Scientists

PhD thesis

Engineers / Software developers

  • Research Engineer - Brain image analysis - Starting date:  March 2024 - Contact: ninon.burgos@cnrs.fr
  • Research Engineer - Deep learning for brain image analysis - Starting date:  as soon as possible - Contact: camille.brianceau@icm- institute.org

 Master 2 Internships / Stages de Master 2

  •  
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If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).

Postdocs / Scientists

PhD thesis

Engineers / Software developers

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If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).

Postdocs / Scientists

PhD thesis

Engineers / Software developers

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If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).

Postdocs / Scientists

PhD thesis

Engineers / Software developers

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The Aramis Lab brings together methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numerical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University and belongs to the Paris Brain Institute (ICM), which is a neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
We are financially supported by these institutions.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical learning, Deep learning, Generative models, Bayesian models [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Magnetic resonance Imaging (MRI), Positron Emission Tomography (PET)[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Riemannian Geometry, Diffeomorphisms[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Graph analysis, Multilayer networks, Network dynamics[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Non-linear mixed effects models, Riemannian geometry, Longitudinal shape models[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Clinical decision support systems, Disease progression models, Network alterations[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Modeling the presymptomatic phase in genetic forms of the disease[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Using multimodal neuroimaging to track disease progression[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Modeling disease progression, Computer-aided adjustment of treatment[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Using BCI for rehabilitation, BCI systems based on network analysis[/su_spoiler] [/su_accordion]
 
Team retreat at the villa Finaly, Florence, Italy
 ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-autosave-v1', '', '', '2023-12-22 19:56:06', '2023-12-22 18:56:06', '', 1098, 'https://www.aramislab.fr/1098-autosave-v1/', 0, 'revision', '', (1686, 9, '2020-09-09 17:59:54', '2020-09-09 16:59:54', '[su_slider source="media: 968,966,1015,958,971" width="1600" height="600" title="no" mousewheel="no"]
The Aramis Lab brings together methodological researchers (computer scientists, applied mathematics) and medical experts (neurology, medical imaging) to build numerical models of brain diseases from multimodal patient data: medical imaging, clinical data and genomic data.
 
The Aramis Lab is a joint research team between CNRS, Inria, Inserm and Sorbonne University and belongs to the Paris Brain Institute (ICM), which is a neuroscience center based in the Pitié-Salpêtrière hospital in Paris, the largest adult hospital in Europe.
The team develops new data representations and statistical learning approaches that can integrate multiple types of data acquired in the living patient, including medical imaging, clinical and genomic data. In turn, these models shall allow for a better understanding of disease progression, and the development of new decision support systems for diagnosis, prognosis and design of clinical trials.
We are financially supported by these institutions.

Key methododological domains

[su_accordion] [su_spoiler title="Machine Learning" open="yes" style="fancy" icon="chevron" class="accordeon"]Statistical learning, Deep learning, Generative models, Bayesian models [/su_spoiler] [su_spoiler title="Medical image processing" style="fancy" icon="chevron" class="accordeon"]Magnetic resonance Imaging (MRI), Positron Emission Tomography (PET)[/su_spoiler] [su_spoiler title="Morphometry and shape analysis" style="fancy" icon="chevron" class="accordeon"]Riemannian Geometry, Diffeomorphisms[/su_spoiler] [su_spoiler title="Complex network theory" style="fancy" icon="chevron" class="accordeon"]Graph analysis, Multilayer networks, Network dynamics[/su_spoiler] [su_spoiler title="Longitudinal analysis" style="fancy" icon="chevron" class="accordeon"]Non-linear mixed effects models, Riemannian geometry, Longitudinal shape models[/su_spoiler] [/su_accordion]

Main applications

[su_accordion] [su_spoiler title="Alzheimer\'s disease" open="yes" style="fancy" icon="chevron" class="accordeon"]Clinical decision support systems, Disease progression models, Network alterations[/su_spoiler] [su_spoiler title="Fronto-temporal dementia" style="fancy" icon="chevron" class="accordeon"]Modeling the presymptomatic phase in genetic forms of the disease[/su_spoiler] [su_spoiler title="Multiple sclerosis" style="fancy" icon="chevron" class="accordeon"]Using multimodal neuroimaging to track disease progression[/su_spoiler] [su_spoiler title="Parkinson\'s disease" style="fancy" icon="chevron" class="accordeon"]Modeling disease progression, Computer-aided adjustment of treatment[/su_spoiler] [su_spoiler title="Brain computer interfaces" style="fancy" icon="chevron" class="accordeon"]Using BCI for rehabilitation, BCI systems based on network analysis[/su_spoiler] [/su_accordion]
 
Team retreat at the villa Finaly, Florence, Italy
 ', 'Home', '', 'inherit', 'closed', 'closed', '', '1098-revision-v1', '', '', '2020-09-09 17:59:54', '2020-09-09 16:59:54', '', 1098, 'https://www.aramislab.fr/1098-revision-v1/', 0, 'revision', '', (1687, 9, '2020-09-09 18:05:59', '2020-09-09 17:05:59', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed capturing various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, multiple sclerosis, Parkinson\'s disease...). They shall allow deepening our understanding of neurological diseases and developing new decision support systems for diagnosis, prognosis and design of clinical trials.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Network theoretic approaches to integrate heterogeneous brain networks

T
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

External collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Local collaborations

Methodological collaborations

[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Funding / main grants

   ', 'Research topics', '', 'inherit', 'closed', 'closed', '', '22-autosave-v1', '', '', '2020-09-09 18:05:59', '2020-09-09 17:05:59', '', 22, 'https://www.aramislab.fr/22-autosave-v1/', 0, 'revision', '', (1688, 9, '2020-09-09 18:06:35', '2020-09-09 17:06:35', '

    Context and general aim

The tremendous progress of neuroimaging, genomic and biomarker technologies has allowed capturing various characteristics of brain diseases in living patients. Collection of multimodal data in large patient databases provides a comprehensive view of brain alterations, biological processes, genetic risk factors and symptoms. The team aims to build numerical models of brain diseases from multimodal patient based on appropriate data-driven approaches. To this end, we develop new data representations and statistical learning approaches that can integrate multiple types of data: neuroimaging, peripheral biomarkers, clinical and omics data (genetics, transcriptomics).
In particular, we develop methods to highlight networks of interactions among multiple sources of data, to track data changes during disease progression, and to automatically predict current or future clinical outcomes from these data. We apply these models to neurodegenerative diseases (Alzheimer\'s disease and other dementias, multiple sclerosis, Parkinson\'s disease...). They shall allow deepening our understanding of neurological diseases and developing new decision support systems for diagnosis, prognosis and design of clinical trials.
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New representations from multimodal medical images

Combining multiple neuroimaging modalities is necessary to obtain a comprehensive picture of alterations in brain diseases (atrophy, anatomical disconnections, functional connectivity alterations, metabolic alterations, abnormal protein deposits…). Such a combination is a non-trivial task because different types of information are conveyed by the different modalities, which in turns leads to different natural data representations (meshes and curves for geometrical information, signals, networks). We propose to build new integrated data representations from multiple modalities. Such representations will be subsequently entered into statistical models and decision support systems. Specifically, we will introduce representations that can integrate geometrical information (anatomical surfaces extracted from anatomical MRI, white matter tracts extracted from diffusion MRI) together with functional (PET, ASL, EEG/MEG) and microstructural information.
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Network theoretic approaches to integrate heterogeneous brain networks

T
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Spatio-temporal models to build trajectories of disease progression from longitudinal data

Longitudinal data sets are collected to capture variable temporal phenomena, which may be due to ageing or disease progression for instance. They consist in the observation of several individuals, each of them being observed at multiple points in time. The statistical exploitation of such data sets is notably difficult since data of each individual follow a different trajectory of changes and at its own pace. Our team has contributed to the definition of a generic theoretical and algorithmic framework for learning typical trajectories from longitudinal data sets. This framework is built on tools from Riemannian geometry and the inference is based on a stochastic Expectation Maximization (EM) algorithm coupled with Markov Chain Monte Carlo methods.
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Decision support systems for diagnosis, prognosis and design of clinical trials

Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
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External collaborations

Methodological collaborations

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Local collaborations

Methodological collaborations

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Funding / main grants

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Most representative publications

Networks

  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017.PDF Paper in PDF
  • Corsi M-C, Chavez M, Schwartz D, George N, Hugueville L, Kahn A E, Dupont S, Bassett D S, De Vico Fallani F, Functional disconnection of associative cortical areas predicts performance during BCI training, NeuroImage, 209: 116500, 2020. PDF Paper in PDF
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Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
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Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM journal on Numerical Analysis 2017. 56(4), 256-2584PDF Paper in PDF
  • Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. PDF Paper in PDF
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Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3):682-696, 2013. PDF Paper in PDF
  • Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. Neuroimage, 183: 504–521, 2018. PDF Paper in PDF
  • Wen J, Thibeau-Sutre E, Samper-González J, Routier A, Bottani S, Durrleman S, Burgos N, Colliot O: Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation, Medical Image Analysis, 63: 101694, 2020 PDF Paper in PDF
  • Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Multimodal Learning for Clinical Decision Support, 357-364. 2017. PDF Paper in PDF
  • Wei, W., Poirion, E., Bodini, B., Durrleman, S., Ayache, N., Stankoff, B., Colliot, O. Predicting PET-derived Demyelination from Multimodal MRI using Sketcher-Refiner Adversarial Training for Multiple Sclerosis, Medical Image Analysis, 58: 101546, 2019. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinical studies

  • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
  • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
  • Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. PDF Paper in PDF
  • Dubois B, Epelbaum S, Nyasse F, Bakardjian H, Gagliardi G, Uspenskaya O, Houot M, Lista S, Cacciamani F, Potier MC, Bertrand A, Lamari F, Benali H, Mangin JF, Colliot O, Genthon R, Habert MO, Hampel H; INSIGHT-preAD study group. Cognitive and neuroimaging features and brain β-amyloidosis in individuals at risk of Alzheimer\'s disease (INSIGHT-preAD): a longitudinal observational study.. In Lancet Neurol.. 2018, Apr;17(4):335-346. PDF Paper in PDF
  • Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-autosave-v1', '', '', '2020-09-09 18:30:49', '2020-09-09 17:30:49', '', 26, 'https://www.aramislab.fr/26-autosave-v1/', 0, 'revision', '', 0); INSERT INTO `wp_aramis_posts` VALUES (1690, 9, '2020-09-09 18:23:35', '2020-09-09 17:23:35', '

Most representative publications

Networks

  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017.PDF Paper in PDF
  • Corsi M-C, Chavez M, Schwartz D, George N, Hugueville L, Kahn A E, Dupont S, Bassett D S, De Vico Fallani F, Functional disconnection of associative cortical areas predicts performance during BCI training, NeuroImage, 209: 116500, 2020. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM journal on Numerical Analysis 2017. 56(4), 256-2584PDF Paper in PDF
  • Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3):682-696, 2013. PDF Paper in PDF
  • Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. Neuroimage, 183: 504–521, 2018. PDF Paper in PDF
  • Wen, J, Thibeau-Sutre, E, Samper-González, J, Routier, A, Bottani, S, Durrleman, S, Burgos, N, Colliot, O: Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation, Medical Image Analysis, 63: 101694, 2020 PDF Paper in PDF
  • Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Multimodal Learning for Clinical Decision Support, 357-364. 2017. PDF Paper in PDF
  • Wei, W., Poirion, E., Bodini, B., Durrleman, S., Ayache, N., Stankoff, B., Colliot, O. Predicting PET-derived Demyelination from Multimodal MRI using Sketcher-Refiner Adversarial Training for Multiple Sclerosis, Medical Image Analysis, 58: 101546, 2019. PDF Paper in PDF
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Clinical studies

  • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
  • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
  • Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. PDF Paper in PDF
  • Dubois B, Epelbaum S, Nyasse F, Bakardjian H, Gagliardi G, Uspenskaya O, Houot M, Lista S, Cacciamani F, Potier MC, Bertrand A, Lamari F, Benali H, Mangin JF, Colliot O, Genthon R, Habert MO, Hampel H; INSIGHT-preAD study group. Cognitive and neuroimaging features and brain β-amyloidosis in individuals at risk of Alzheimer\'s disease (INSIGHT-preAD): a longitudinal observational study.. In Lancet Neurol.. 2018, Apr;17(4):335-346. PDF Paper in PDF
  • Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. PDF Paper in PDF
 

Full list of publications

Here is a link to our publications on the open archive HAL.  ', 'Publications', '', 'inherit', 'closed', 'closed', '', '26-revision-v1', '', '', '2020-09-09 18:23:35', '2020-09-09 17:23:35', '', 26, 'https://www.aramislab.fr/26-revision-v1/', 0, 'revision', '', (1691, 9, '2020-09-09 18:36:59', '2020-09-09 17:36:59', '

Clinica

Clinica
References
  • Routier, A., Guillon, J., Burgos, N., Samper-Gonzalez, J., Wen, J., Fontanella, S., Bottani, S., Jacquemont, T., Marcoux, A., Gori, P., Lu, P., Moreau, T., Bacci, M., Durrleman, S., Colliot, O. Clinica: an open source software platform for reproducible clinical neuroscience studies. In OHBM 2018. PDF Paper in PDF
  • Routier A, Marcoux A, Diaz Melo M, Guillon J, Samper-González J, Wen J, Bottani S, Guyot A, Thibeau-Sutre E, Teichmann M, Habert M-O, Durrleman S, Burgos N, Colliot O: New Advances in the Clinica Software Platform for Clinical Neuroimaging Studies. In OHBM 2019. PDF Paper in PDF
  • Routier A, Marcoux A, Diaz Melo M, Samper-González J, Wild A, Guyot A, Wen J, Thibeau- Sutre E, Bottani S, Durrleman S, Burgos N, Colliot O: New Longitudinal and Deep Learning Pipelines in the Clinica Software Platform. In OHBM, 2020. PDF Paper in PDF
  • Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. Neuroimage, 183: 504–521, 2018. PDF Paper in PDF
  • Wen J, Thibeau-Sutre E, Samper-González J, Routier A, Bottani S, Durrleman S, Burgos N, Colliot O: Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation, Medical Image Analysis, 63: 101694, 2020 PDF Paper in PDF
  • Marcoux A, Burgos N, Bertrand A, Teichmann M, Routier A, Wen J, Samper-González J, Bottani S, Durrleman S, Habert M-O, Colliot O: An Automated Pipeline for the Analysis of PET Data on the Cortical Surface’. Frontiers in Neuroinformatics, 12, 2018. PDF Paper in PDF
 
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Deformetrica

References  
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]  
 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-autosave-v1', '', '', '2020-09-09 18:36:59', '2020-09-09 17:36:59', '', 620, 'https://www.aramislab.fr/620-autosave-v1/', 0, 'revision', '', (1692, 9, '2020-09-09 18:37:10', '2020-09-09 17:37:10', '

Clinica

Clinica
 
[su_button url="http://www.clinica.run/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:olivier.colliot(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Deformetrica

Clinica
Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.
References
  • S. Durrleman, S., Prastawa, M., Charon, N., Korenberg, J.R., Joshi, S., Gerig, G., Trouvé, A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters.. In Neuroimage 101(1): 35-49, 2014 PDF Paper in PDF
  • Bône, A., Louis, M., Martin, B., & Durrleman, S. Deformetrica 4: an open-source software for statistical shape analysis. In nternational Workshop on Shape in Medical Imaging Springer, Cham, 2018. p. 3-13. PDF Paper in PDF
 
[su_button url="http://www.deformetrica.org/" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:stanley.durrleman(a)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Brain network toolbox

Logo_alto_100
A list of MATLAB routines for characterizing brain network topology though graph theoretical indices can be found at the website of the FreeBorN consortium, which promotes the interaction and visibility of the research teams studying brain connectivity and network theory. 
[su_button url="https://sites.google.com/site/fr2eborn/download" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:fabrizio.devicofallani(a)gmail(dot)com" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
[su_divider style="default" divider_color="#702082" top="no" link_color="#702082" size="1" margin="16"]

Leasp

To come soon References
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
 
[su_button url="https://gitlab.icm-institute.org/aramislab/leasp" size="5" style="flat" background="#702082" color="white" center="yes"]Website[/su_button]
[su_button url="mailto:stanley.durrleman(at)inria(dot)fr" size="5" style="flat" background="#702082" color="white" center="yes"]Contact[/su_button]
 ', 'Software', '', 'inherit', 'closed', 'closed', '', '620-revision-v1', '', '', '2020-09-09 18:37:10', '2020-09-09 17:37:10', '', 620, 'https://www.aramislab.fr/620-revision-v1/', 0, 'revision', '', (1693, 9, '2020-09-09 18:37:14', '2020-09-09 17:37:14', '

Most representative publications

Networks

  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philosophical Transactions of the Royal Society B: Biological Sciences 369:1653, 20130521–20130521, 2014. PDF Paper in PDF
  • De Vico Fallani F, Latora V, Chavez, M. A Topological Criterion for Filtering Information in Complex Brain Networks. PLOS Computational Biology, 13(1), e1005305, 2017.PDF Paper in PDF
  • De Vico Fallani F, Corazzol M, Sternberg JR, Wyart C, Chavez M. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(3):333-341, 2014. PDF Paper in PDF
  • Obando C, De Vico Fallani F. A statistical model for brain networks inferred from large-scale electrophysiological signals. Journal of the Royal Society Interface (Accepted for Publication), 2017. PDF Paper in PDF
  • De Vico Fallani F, Nicosia V, Latora V, Chavez M. Non-parametric resampling of random walks for spectral networks clustering. Physical Review E 89, 012802, 2013. PDF Paper in PDF
  • Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F Loss of brain inter-frequency hubs in Alzheimer\'s disease. In Scientific Reports 7;10879. 2017.PDF Paper in PDF
  • Corsi M-C, Chavez M, Schwartz D, George N, Hugueville L, Kahn A E, Dupont S, Bassett D S, De Vico Fallani F, Functional disconnection of associative cortical areas predicts performance during BCI training, NeuroImage, 209: 116500, 2020. PDF Paper in PDF
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Longitudinal

  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Advances in Neural Information Processing Systems pp. 2395–2403, 2015. PDF Paper in PDF
  • Schiratti J-B, Allassonniere S, Colliot O, Durrleman S.A Bayesian mixed-efects model to learn trajectories of changes from repeated manifold-valued observations. In Journal of Machine Learning Research (JMLR) 18(1), 4840-4872. 2017. PDF Paper in PDF
  • Koval I, Schiratti JB, Routier A, Bacci M, Colliot M, Allassonnière S, Durrleman S. Spatiotemporal propagation of the cortical atrophy: population and individual patterns. In Frontiers in Neurology 9, 2018. PDF Paper in PDF
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Morphometry

  • Durrleman S, Pennec X, Trouvé A, Braga J, Gerig G, Ayache N. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision 103(1):22-59, 2013. PDF Paper in PDF
  • Durrleman S, Prastawa M, Charon N, Korenberg JR, Joshi S, Gerig G, Trouvé A. Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101:35–49, 2014. PDF Paper in PDF
  • Gori P, Colliot O, Marrakchi-Kacem L, Worbe Y, Poupon, C, Hartmann A, Ayache N, Durrleman S. A Bayesian framework for joint morphometry of surface and curve meshes in multi-object complexes. Medical Image Analysis , 35, 458-474, 2017.PDF Paper in PDF
  • Louis M, Charlier B, Jusselin P, Susovan P, Durrleman S. A fanning scheme for the parallel transport along geodesicson Riemmanian manifolds. In SIAM journal on Numerical Analysis 2017. 56(4), 256-2584PDF Paper in PDF
  • Bone A, Colliot O, Durrleman SLearning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms. In Computer Vision and Pattern Recognition (CVPR) 9271-9280, 2018. PDF Paper in PDF
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Machine Learning

  • Cuingnet R, Glaunès JA, Chupin M, Benali H, Colliot O. The ADNI. Spatial and anatomical regularization of SVM: a general framework for neuroimaging data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(3):682-696, 2013. PDF Paper in PDF
  • Samper-González J, Burgos N, Bottani S, Fontanella S, Lu P, Marcoux A, Routier A, Guillon J, Bacci M, Wen J, Bertrand A, Bertin H, Habert MO, Durrleman S, Evgeniou T, Colliot O. Reproducible evaluation of classification methods in Alzheimer\'s disease: Framework and application to MRI and PET data. Neuroimage, 183: 504–521, 2018. PDF Paper in PDF
  • Wen J, Thibeau-Sutre E, Samper-González J, Routier A, Bottani S, Durrleman S, Burgos N, Colliot O: Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation, Medical Image Analysis, 63: 101694, 2020 PDF Paper in PDF
  • Ansart A, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Prediction of amyloidosis from neuropsychological and MRI data for cost effective inclusion of pre-symptomatic subjects in clinical trials. In Multimodal Learning for Clinical Decision Support, 357-364. 2017. PDF Paper in PDF
  • Wei, W., Poirion, E., Bodini, B., Durrleman, S., Ayache, N., Stankoff, B., Colliot, O. Predicting PET-derived Demyelination from Multimodal MRI using Sketcher-Refiner Adversarial Training for Multiple Sclerosis, Medical Image Analysis, 58: 101546, 2019. PDF Paper in PDF
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Clinical studies

  • Dubois B, Chupin M, Hampel H, Lista S, Cavedo E, Croisile B, Tisserand GL, Touchon J, Bonafe A, Ousset PJ, Ait Ameur A, Rouaud O, Ricolfi F, Vighetto A, Pasquier F, Delmaire C, Ceccaldi M, Girard N, Lehéricy S, Tonelli I, Duveau F, Colliot O, Garnero L, Sarazin M, Dormont D. Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer’s disease. Alzheimer’s & Dementia 11(9):1041–1049, 2015. PDF Paper in PDF
  • Hamelin L, Bertoux M, Bottlaender M, Corne H, Lagarde J, Hahn V, Mangin JF, Dubois B, Chupin M, Cruz de Souza L, Colliot O, Sarazin M, Sulcal morphology as a new imaging marker for the diagnosis of early onset Alzheimer\'s disease, Neurobiology of Aging, 36(11):2932-9, 2015 PDF Paper in PDF
  • Betrand A, Wen J, Rinaldi D, Houot M, Sayah S, Camuzat A, Fournier C, Fontanella S, Routier A, Couratier P, Pasquier F, Habert MO, Hannequin D, Martinaud O, Caroppo P, Levy R, Dubois B, Brice A, Durrleman S, Colliot O. Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years. In JAMA neurology 75(2);236-245, 2018. PDF Paper in PDF
  • Dubois B, Epelbaum S, Nyasse F, Bakardjian H, Gagliardi G, Uspenskaya O, Houot M, Lista S, Cacciamani F, Potier MC, Bertrand A, Lamari F, Benali H, Mangin JF, Colliot O, Genthon R, Habert MO, Hampel H; INSIGHT-preAD study group. Cognitive and neuroimaging features and brain β-amyloidosis in individuals at risk of Alzheimer\'s disease (INSIGHT-preAD): a longitudinal observational study.. In Lancet Neurol.. 2018, Apr;17(4):335-346. PDF Paper in PDF
  • Wen J, Zhang H, Alexander DC, Durrleman S, Routier A, Rinaldi D, Jouot M, Couratier P, Hannequin D, Pasquier F, Zhang J, Colliot O, Le Ber I, Bertrand A. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. In J Neurol Neurosurg Psychiatry 318994. 2018. PDF Paper in PDF
 

Full list of publications

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Faculty

[tmm name="faculty"]

Post-docs

[tmm name="post-docs"]

PhD Students

[tmm name="phd-students"]

Engineers

[tmm name="engineers"]

Associate fellows

[tmm name="collaborators"]

Administrative staff

[tmm name="assistant"]

Former Members

Left in 2020 :
  • Alexandre Bône - Former PhD student. Now Augmented Intelligence Specialist at Guerbet
  • Manon Ansart - Former PhD student. Now Postdoctoral fellow at the LIO laboratory, hosted at ETS, Montreal
  • Arnaud Marcoux - Former engineer
  • Adam Wild - Former Engineer
  • Vincent Henry - Former Postdoctoral fellow
  • Emmannuel Mauduit  - LinkedIn - Former coordinator the ICM center of  Neuroinformatics
  Left in 2019 :
  • Maxime Louis - Former PhD Student, now at Pixyl
  • Pascal Lu - Former PhD student
  • Benoit Martin - Former engineer. Now PhD student
  • Alexis Guyot - Former Postdoctoral fellow
  • Junhao Wen - Former PhD student
  • Fanny Grosselin - Former PhD student
  • Jorge Samper-González - Former PhD student & Postdoctoral fellow, now data scientist at Qynapse
  Left in 2018 and before :
  • Catalina Obando - Former PhD student
  • Jeremy Guillon - Former PhD student - webpage
  • Michael Bacci - Former engineer, now at EURONEXT
  • Sabrina Fontanella - Former engineer
  • Clementine Fourrier - Former engineer, now at SED INRIA
  • Federico Battiston - Former visiting researcher
  • Takoua Kaaouana - Former postdoctoral fellow
  • Xavier Navarro - Former postdoctoral fellow, now researcher at MyBrainTechnologies
  • Romain Valabrègue - Former Inserm Research Engineer
  • Eric Bardinet - Former CNRS Research Engineer - webpage
  • Sara Fernandez-Vidal - Former Inserm Research Engineer - webpage
  • Vera Dinkelacker - Former associated clinician
  • Thomas Estienne -  Former administrative assistant
  • Chabha Azouani - Former clinical research associate
  • Kelly Martineau - Former clinical research associate
  • Sonia Djobeir - Former clinical research associate
  • Hugo Dary - Former engineer
  • Ludovic Fillon - Former engineer
  • Mathieu Dubois - Former engineer
  • Barbara Gris - Former PhD student
  • Géraldine Rousseau -Former PhD student
  • Marika Rudler - Former PhD student
  • Jean-Baptiste Schiratti - Former PhD student
  • Pietro Gori - Former PhD student - Now Assistant Professor, Telecom ParisTech - webpage
  • Ana Fouquier - Former postdoctoral fellow - Now R&D Engineer, Cardiawave Company
  • Soledad Fernandez-Garcia - Former postdoctoral fellow - Now Assistant Professor, University of Sevilla
  • Tristan Moreau - Former postdoctoral fellow - Now Postdoctoral fellow at ICM Dubois/Lévy\'s team
  • Susovan Pal - Former Postdoctoral fellow - Now Postdoctoral fellow at UCLA (University of California at Los Angeles)
  • François Touvet - Former engineer - Now Pipeline TD Junior at Ellipse Studio
  • Alexis Mocellin - Former engineer
  • Claire Cury - Former PhD student - Now Postdoctoral fellow at UCL (University College London)
  • Johanne Germain - Former clinical research associate -  Now Clinical Research Associate in Toulouse
  • Ali Bouyahia - Former clinical research associate - Now Psychiatrist
  • Linda Marrakchi-Kacem - Former Postdoctoral fellow - Now Assistant Professor, Tunisia
  • Xavier Badé - Clinical Research Associate - Now Clinical Research Assistant at Umanis
  • Yohan Attal - Postdoctoral fellow - Now CEO of MyBrainTechnologies
  • Mario Chavez - CNRS Researcher (CR1)
  • Claude Adam - Neurologist (PH), AP-HP
  • Sophie Dupont - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Damien Galanaud - Professor of Neuroradiology (PU-PH), UPMC / AP-HP
  • Dominique Hasboun - Associate Professor of Neuroanatomy (MCU), UPMC
  • Yves Samson - Professor of Neurology (PU-PH), UPMC / AP-HP
  • Lionel Thivard - Neurologist (PH), AP-HP
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If you are interested in joining the team, do not hesitate to send us an e-mail stating your field of interest and a CV. We may have other open positions than those advertised below. You can directly contact one of the faculty members of the team, or the head of the team for a general inquiry. Our team is pluridisciplinary and we welcome applicants with different profiles and expertises (computer science, electrical engineering, neuroimaging, neuroscience, medicine...).

Postdocs / Scientists

PhD thesis

Engineers / Software developers

 Master Internships / Stages de Master

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